From 627e0eb8eb7f9330a66d361594e2440827acb797 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 7 Nov 2024 15:30:22 +0100 Subject: [PATCH 01/44] Initial commit --- ...tion-related-data-analytics-with-fabric.md | 99 ++++++++++++++++++ .../automation-related-table-relations.png | Bin 0 -> 78739 bytes .../fabric-toolset.png | Bin 0 -> 549536 bytes 3 files changed, 99 insertions(+) create mode 100644 articles/advanced-automation-related-data-analytics-with-fabric.md create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md new file mode 100644 index 000000000..a14381205 --- /dev/null +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -0,0 +1,99 @@ +--- +title: Advanced automation-related data analytics with the Dataverse - Fabric integration +description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports and alerts at scale. +ms.topic: conceptual +ms.date: 11/06/2024 +ms.author: appapaio +ms.reviewer: dmartens +contributors: + - DanaMartens +author: rpapostolis +ms.collection: conceptual +search.audienceType: + - admin + - coe + - flowmaker + - enduser +--- + +# Advanced automation-related data analytics with Fabric + +Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](/articles/automation-center-overview.md) and [Desktop Flow Activity](/articles/desktop-flows/desktop-flow-activity.md). + +However, your organization may have more advanced or custom monitoring, reporting, and analysis needs that are not covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable and compliant analytics solutions using data from Power Automate and many other sources. + +## Microsoft Fabric as your analytics platform + +[Microsoft Fabric](/fabric/get-started/microsoft-fabric-overview) is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a unified suite of analytical services including Data Engineering, Data Science, Real-Time Analytics, Data Warehousing with advanced data governance and security control. + +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: + +## Dataverse direct link to Microsoft Fabric + +The Link to Microsoft Fabric feature built into Power Platform makes all your Dynamics 365, Power Apps and Power Automate data available in [Microsoft OneLake](/fabric/onelake/onelake-overview), the built-in data lake for Microsoft Fabric. Key benefits of the Dataverse direct link: + +- No need to export data, build extract, transform, load (ETL) pipelines, or use our partner integration tools. +- With shortcuts from Dataverse directly into OneLake, your data stays in Dataverse while authorized users get to work with data in Fabric. +- Link data from all Dynamics 365 apps, including Dynamics 365 Finance and Operations apps. +- Build Power Apps and automations to drive action from insights in OneLake. + +## Get started + +In the following guide, we will walk you through the process of leveraging Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you will have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. + +[> !NOTE] +> +> **Disclaimer:** The scenarios, code examples, and data used in this tutorial are fictional, may include errors and intended solely for demonstration purposes. They do not represent product or analysis recommendations. + +## Prerequisites + +Before you continue with this tutorial, ensure you meet the following prerequisites: + +1. Have a basic understanding of data modelling, Power Automate, Dataverse, and the Fabric ecosystem. +2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud and desktop flow runs, and System Administrator role in this environment. +3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. +4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. +5. If you plan to follow the advanced section for [Desktop flow action log-level analytics](#desktop-flow-action-log-level-analytics), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. + +## List of tables are automation-related + +The following table incudes automation-related tables that are frequently used for reporting and observability use cases. + +| Display name | Object name | Purpose | +|--------------------|------------------|-------------------------------------------------------| +| [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | +| [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | +| [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | +| [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | +| [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | +| [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | +| [Work Queue Item](/power-apps/developer/data-platform/reference/entities/workqueueitem) | workqueueitem | Contains information about each run of a workflow.| +| [User](/power-apps/developer/data-platform/reference/entities/systemuser) | systemuser | Represents an instance of a workflow execution. | + +### Simplified table relationship diagram + +The image includes only relations to tables that are included above and automation-relevant. + +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png"::: + +## Creating automation-related queries + +### Performance-related query examples + +### Governance-related query examples + +## Creating reports in Fabric + +### Creating real-time alerts + +## Persisting telemetry data in a Fabric Warehouse + +### Creating a Dataflow Gen2 to ingest data to a warehouse + +### Desktop flow action log-level analytics + +[> NOTE!] +> +> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. + +## Closing diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png b/articles/media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png new file mode 100644 index 0000000000000000000000000000000000000000..a943bd231cfdb79422443c5718532d1d06a1fbd1 GIT binary patch literal 78739 zcmd?Rc{G;q`!@P$CPkC+9Or#qM@w}R%?=t0g|g|W znu;EUvf6+`Syi)P9bQ@9Q|^y{sGZbKxl$-B&E!8S=Ejd>cyYbkQO(2ae{5X8iA{2~ zi?8^;UU3i^_ysqr%2>H1w2s zxXD-4QT2@fyiw#Rbxa8PQrp9y%<%7R)w_8N{=JJkE;Ug7-`jRa-`2VQ?`;-Q|Ns4C zUZZRQScKxUrGANruC9xWh%lMZQDOJ|`{!56iRY*1HY@M0B#RiS+HCCqx8f#?XogPm zm15;BUJnwbFW!jaIVO-CrTT9Hy;4sJZX2vmw4D%AVYeA;f0gU8f!jSLP~C;6TnL|$ z<-t5n5irbpP^?UkEmRF*5U7s1M#oWK6}fAS?V4;r^9y5bHMNl9@_#=LBtL$bl0r>O 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a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -41,7 +41,7 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy In the following guide, we will walk you through the process of leveraging Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you will have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. -[> !NOTE] +> [!NOTE] > > **Disclaimer:** The scenarios, code examples, and data used in this tutorial are fictional, may include errors and intended solely for demonstration purposes. They do not represent product or analysis recommendations. From 419e1b367d66ba00582f469e3b3c5f63fb773575 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 7 Nov 2024 15:45:07 +0100 Subject: [PATCH 03/44] Acrolinx changes --- ...-automation-related-data-analytics-with-fabric.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index b78435a02..0910963ae 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -1,6 +1,6 @@ --- title: Advanced automation-related data analytics with the Dataverse - Fabric integration -description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports and alerts at scale. +description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. ms.topic: conceptual ms.date: 11/06/2024 ms.author: appapaio @@ -20,7 +20,7 @@ search.audienceType: Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](/articles/automation-center-overview.md) and [Desktop Flow Activity](/articles/desktop-flows/desktop-flow-activity.md). -However, your organization may have more advanced or custom monitoring, reporting, and analysis needs that are not covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable and compliant analytics solutions using data from Power Automate and many other sources. +However, your organization may have more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable, and compliant analytics solutions using data from Power Automate and many other sources. ## Microsoft Fabric as your analytics platform @@ -39,7 +39,7 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy ## Get started -In the following guide, we will walk you through the process of leveraging Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you will have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. +In the following guide, we'll walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you'll have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. > [!NOTE] > @@ -49,15 +49,15 @@ In the following guide, we will walk you through the process of leveraging Datav Before you continue with this tutorial, ensure you meet the following prerequisites: -1. Have a basic understanding of data modelling, Power Automate, Dataverse, and the Fabric ecosystem. -2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud and desktop flow runs, and System Administrator role in this environment. +1. Have a basic understanding of data modeling, Power Automate, Dataverse, and the Fabric ecosystem. +2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. 3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. 4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. 5. If you plan to follow the advanced section for [Desktop flow action log-level analytics](#desktop-flow-action-log-level-analytics), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## List of tables are automation-related -The following table incudes automation-related tables that are frequently used for reporting and observability use cases. +The following table includes automation-related tables that are frequently used for reporting and observability use cases. | Display name | Object name | Purpose | |--------------------|------------------|-------------------------------------------------------| From 5e69310b22e72feee25420696bff8f2aba64abed Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 7 Nov 2024 15:46:17 +0100 Subject: [PATCH 04/44] Update advanced-automation-related-data-analytics-with-fabric.md --- .../advanced-automation-related-data-analytics-with-fabric.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 0910963ae..43412830f 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -92,7 +92,7 @@ The image includes only relations to tables that are included above and automati ### Desktop flow action log-level analytics -[> NOTE!] +> [!NOTE] > > Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. From 855915b3406ce39450d1a904232b030868eb0013 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 7 Nov 2024 17:29:36 +0100 Subject: [PATCH 05/44] Update advanced-automation-related-data-analytics-with-fabric.md --- .../advanced-automation-related-data-analytics-with-fabric.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 43412830f..4cff545ce 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -1,5 +1,5 @@ --- -title: Advanced automation-related data analytics with the Dataverse - Fabric integration +title: Automation-related data analytics with the Dataverse - Fabric integration description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. ms.topic: conceptual ms.date: 11/06/2024 @@ -16,7 +16,7 @@ search.audienceType: - enduser --- -# Advanced automation-related data analytics with Fabric +# Automation-related data analytics with Fabric Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](/articles/automation-center-overview.md) and [Desktop Flow Activity](/articles/desktop-flows/desktop-flow-activity.md). From 1c8da94ce6f81a057cb86ac35e59e17dc0d31e83 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Fri, 8 Nov 2024 15:16:23 +0100 Subject: [PATCH 06/44] Update --- ...mation-related-data-analytics-with-fabric.md | 6 ++++-- .../fabric-workspace-with-lakehouse.png | Bin 0 -> 54598 bytes 2 files changed, 4 insertions(+), 2 deletions(-) create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 4cff545ce..780f7deed 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -47,12 +47,13 @@ In the following guide, we'll walk you through the process of using Dataverse sh ## Prerequisites -Before you continue with this tutorial, ensure you meet the following prerequisites: +Before you continue, ensure you meet the following prerequisites: 1. Have a basic understanding of data modeling, Power Automate, Dataverse, and the Fabric ecosystem. 2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. 3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. -4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. +4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you will see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "dataverse," followed by your environment name and a unique environment suffix, such as ` dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: 5. If you plan to follow the advanced section for [Desktop flow action log-level analytics](#desktop-flow-action-log-level-analytics), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## List of tables are automation-related @@ -64,6 +65,7 @@ The following table includes automation-related tables that are frequently used | [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | | [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | | [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | +| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | | [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png b/articles/media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png new file mode 100644 index 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z+t=wQZU;F`Q={HD%0+jy)KM7L5_dL_)wVe;uP~wJ(%qU%u_KL4hl?Lc7n6N_+DcrC z;!+xIhMwT4S7#T3&r7)vT5n6u4EyOF{>AR7lw9=gXvvm_i*c~CGm%ocod=|zFJz3I zI+O6t{{!!z{?_>r=9>1qCwWwX!`#o@0#BkVp$&K7H>&WvxLvVHV8cjzlc zPldB@w$ASxBU2yK)I?X$gDAb1V?`p@1`d2Izh@%I&K*`9xDW5#wK(Z4UD>{PhJ!KE z=Eqwf$%Y?a3_8;8^_f|Q7V8~o)o1Usdma_nv3=^}o&Y$UfJsvusp=)O{)bP?YBvy< z>=HX~e=)RJ{@vPu^680bzuWTGp@;G=$F^^_ZcTRKar Date: Sat, 9 Nov 2024 07:38:57 +0100 Subject: [PATCH 07/44] SQL analytics endpoint --- ...tion-related-data-analytics-with-fabric.md | 48 +++++++++++++++++- .../lakehouse-renaming.png | Bin 0 -> 93103 bytes 2 files changed, 47 insertions(+), 1 deletion(-) create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 780f7deed..b4fd6ce0b 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -54,7 +54,10 @@ Before you continue, ensure you meet the following prerequisites: 3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. 4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you will see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "dataverse," followed by your environment name and a unique environment suffix, such as ` dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: -5. If you plan to follow the advanced section for [Desktop flow action log-level analytics](#desktop-flow-action-log-level-analytics), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +5. (Optional) Select Lakehouse setting and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of Fabric workspace showing the settings panel for a lakehouse with description." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: +6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#desktop-flow-action-log-level-analytics), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## List of tables are automation-related @@ -80,6 +83,49 @@ The image includes only relations to tables that are included above and automati ## Creating automation-related queries +Step-by-step instructions to create a sample SQL Query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. + +1. **Open Microsoft Fabric:** + - Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com). + - Log in with your credentials. +2. **Access the SQL Analytical Endpoint:** + - Choose the workspace where your Lakehouse is located and click on the desired SQL Analytical Endpoint (a sub-node of your Lakehouse). +3. **Open the SQL Query Editor:** + - Once in the SQL Analytical Endpoint, locate and click on the "New SQL query" button to open the SQL query editor interface. +4. **Write Your SQL Query:** + - In the SQL query editor, enter your SQL query and click **Run**. For example: + + ```sql + SELECT + flowsessionid, + statuscode, + startedon, + completedon, + errorcode, + errormessage, + sessionusername, + runexecutionduration, + runduration, + runwaitduration, + context + FROM + flowsessions + WHERE + regardingobjectid = '[specific_flow_id]' -- Replace with the actual flow ID + AND machineid = '[specific_machine_guid]' -- Replace with the actual machine ID + AND statuscode = 8 -- 'Failed' sessions + AND createdon >= DATEADD(day, -7, GETDATE()) + ORDER BY + createdon DESC; + ``` + + - This example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine Id that have failed within the last 7 days. +5. **Review and Save the Results:** + - Review the query results to ensure they meet your requirements. + - If desired, you can open a Live-query with results in Excel by highlighting the SQL query and selecting "Open in Excel" in the query output section. This will generate and download an Excel file with a Live-query to the SQL Analytics endpoint to further deep-dive on the results. +6. **(Optional) Save the Query:** + - If you want to save the SQL query for future use, click on the "Save Query" button. + ### Performance-related query examples ### Governance-related query examples diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png b/articles/media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png new file mode 100644 index 0000000000000000000000000000000000000000..b62df33ef54ffdda3117cfe2fdfdeeb40842cb8d GIT binary patch literal 93103 zcmZU51z1#T*EWiPk_svyrBc!<9Y-ZZ6cji3lU8CWN;A6R_d8J)*{XoOmxD@i$rU`4~Z+;KCrVjvam+`n%(gj z4&mCuAu)X$U29`YTVo4zG@>hasNpy^>i8oIODAh%LnB+XOo9#pIEw%KsI|T}>Z`YH zjm`AY?(Y*^flscWK6zLMc_izmB&-r%Ny839l=UyxSe(jNknVG)1E!tj3WHlVaM14)(*w$1Z?aO*E zJsR3=G%2wM@{VzW>>R*m;?`Y$R=3i}S4MY@`Y%jFyc2 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create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index b4fd6ce0b..41044e7ef 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -54,7 +54,7 @@ Before you continue, ensure you meet the following prerequisites: 3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. 4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you will see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "dataverse," followed by your environment name and a unique environment suffix, such as ` dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: -5. (Optional) Select Lakehouse setting and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. +5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of Fabric workspace showing the settings panel for a lakehouse with description." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: 6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. 7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#desktop-flow-action-log-level-analytics), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. @@ -93,7 +93,7 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End 3. **Open the SQL Query Editor:** - Once in the SQL Analytical Endpoint, locate and click on the "New SQL query" button to open the SQL query editor interface. 4. **Write Your SQL Query:** - - In the SQL query editor, enter your SQL query and click **Run**. For example: + - In the SQL query editor, enter your SQL query and click **Run**. The following example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine Id that have failed within the last 7 days. ```sql SELECT @@ -118,11 +118,29 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End ORDER BY createdon DESC; ``` + + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: + - Here's a list of available status reasons (statuscode) for the `Flow Sessions` (desktop flow runs) table. + + | Status reason | Value | + |-----------|-------| + | Paused | 1 | + | Running | 2 | + | Waiting | 3 | + | Succeeded | 4 | + | Skipped | 5 | + | Suspended | 6 | + | Cancelled | 7 | + | Failed | 8 | + | Faulted | 9 | + | TimedOut | 10 | + | Aborted | 11 | + | Ignored | 12 | - - This example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine Id that have failed within the last 7 days. 5. **Review and Save the Results:** - Review the query results to ensure they meet your requirements. - If desired, you can open a Live-query with results in Excel by highlighting the SQL query and selecting "Open in Excel" in the query output section. This will generate and download an Excel file with a Live-query to the SQL Analytics endpoint to further deep-dive on the results. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: 6. **(Optional) Save the Query:** - If you want to save the SQL query for future use, click on the "Save Query" button. diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png b/articles/media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png new file mode 100644 index 0000000000000000000000000000000000000000..f26bf844c53ac87d3d2622706b44978c22d9f86f GIT binary patch literal 329771 zcmZU52V7H0_cpEy3IeMlRX{{Qq^;7ssPx`DsPtY#57(e*GP1LHGP2WG 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articles/media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 41044e7ef..d8da42e1a 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -85,15 +85,10 @@ The image includes only relations to tables that are included above and automati Step-by-step instructions to create a sample SQL Query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. -1. **Open Microsoft Fabric:** - - Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com). - - Log in with your credentials. -2. **Access the SQL Analytical Endpoint:** - - Choose the workspace where your Lakehouse is located and click on the desired SQL Analytical Endpoint (a sub-node of your Lakehouse). -3. **Open the SQL Query Editor:** - - Once in the SQL Analytical Endpoint, locate and click on the "New SQL query" button to open the SQL query editor interface. -4. **Write Your SQL Query:** - - In the SQL query editor, enter your SQL query and click **Run**. The following example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine Id that have failed within the last 7 days. +1. Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com) and log in with your credentials. +2. Choose the workspace where your Lakehouse is located and click on the desired SQL Analytical Endpoint (a sub-node of your Lakehouse). +3. Once in the SQL Analytical Endpoint, locate and click on the "New SQL query" button to open the SQL query editor interface. +4. In the SQL query editor, enter your SQL query and click **Run**. The following example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine Id that have failed within the last 7 days. ```sql SELECT @@ -119,8 +114,7 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End createdon DESC; ``` - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: - - Here's a list of available status reasons (statuscode) for the `Flow Sessions` (desktop flow runs) table. +5. Here's a list of available status reasons (statuscode) for the `Flow Sessions` (desktop flow runs) table. | Status reason | Value | |-----------|-------| @@ -137,17 +131,311 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End | Aborted | 11 | | Ignored | 12 | -5. **Review and Save the Results:** - - Review the query results to ensure they meet your requirements. - - If desired, you can open a Live-query with results in Excel by highlighting the SQL query and selecting "Open in Excel" in the query output section. This will generate and download an Excel file with a Live-query to the SQL Analytics endpoint to further deep-dive on the results. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: -6. **(Optional) Save the Query:** - - If you want to save the SQL query for future use, click on the "Save Query" button. +6. Review the query results to ensure they meet your requirements. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: +7. (Optional) You can open a Live-query with results in Excel by highlighting the SQL query and selecting "Open in Excel" in the query output section. This will generate and download an Excel file with a Live-query to the SQL Analytics endpoint to further deep-dive on the results. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: +8. (Optional) To store the SQL query for future use, click on the "Save Query" button. -### Performance-related query examples +### Performance-related query example + +This query retrieves the minimum, mean (average), maximum, and standard deviation of runtimes for desktop flow runs (Flow Sessions) of a specified desktop flow, with the runtimes converted from milliseconds rounded up to the nearest full second. The results are grouped by machine IDs and include additional details such as machine names, management types, maximum hosted machine counts, session capacity and the last heartbeat date etc. from the Machine Group and Machine tables. + +```sql + SELECT + f.machineid, + fm.name AS machine_name, + CASE + WHEN mg.managementtype = 0 THEN 'Regular Machine (Group)' + ELSE 'Hosted Machine (Group)' + END AS managementtype, + mg.maxmanagedmachinecount AS maxmanagedmachinecount, + fm.lastheartbeatdate AS last_heartbeat_date, + fm.sessioncapacity AS 'Max Parallel Sessions', + CEILING(MIN(f.runduration) / 1000.0) AS min_runtime, + CEILING(AVG(f.runduration) / 1000.0) AS mean_runtime, + CEILING(MAX(f.runduration) / 1000.0) AS max_runtime, + CEILING(STDEV(f.runduration) / 1000.0) AS stdev_runtime + FROM + flowsession f + JOIN + flowmachinegroup mg ON f.machinegroupid = mg.flowmachinegroupid + JOIN + flowmachine fm ON f.machinegroupid = fm.flowmachinegroupid + WHERE + f.regardingobjectid = '[specific_flow_id]' -- Replace with the actual flow ID + GROUP BY + f.machineid, fm.name, mg.managementtype, mg.maxmanagedmachinecount, fm.lastheartbeatdate, fm.sessioncapacity + ORDER BY + mean_runtime DESC; +``` + +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png"::: + +### Capacity and bottleneck-related query + + +```sql + SELECT + f.machineid, + fm.name AS machine_name, + CASE + WHEN mg.managementtype = 0 THEN 'Regular Machine (Group)' + ELSE 'Hosted Machine (Group)' + END AS managementtype, + mg.maxmanagedmachinecount AS maxmanagedmachinecount, + fm.lastheartbeatdate AS last_heartbeat_date, + fm.sessioncapacity AS 'Max Parallel Sessions', + fm.overcapacitysince, + CASE + WHEN fm.overcapacitysince IS NOT NULL THEN 'Over Capacity' + ELSE 'Within Capacity' + END AS capacity_status + FROM + flowsession f + JOIN + flowmachinegroup mg ON f.machinegroupid = mg.flowmachinegroupid + JOIN + flowmachine fm ON f.machinegroupid = fm.flowmachinegroupid + WHERE + f.regardingobjectid = '[specific_flow_id]' -- Replace with the actual flow ID + GROUP BY + f.machineid, fm.name, mg.managementtype, mg.maxmanagedmachinecount, fm.lastheartbeatdate, fm.sessioncapacity, fm.overcapacitysince + ORDER BY + capacity_status DESC, fm.lastheartbeatdate DESC; + +``` ### Governance-related query examples +### Base desktop flow query with owner info + +This query returns all desktop flows with their owner information. + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6; -- Only consider desktop flows (category 6) +``` + +### Find scripts that include the term `password` or `pwd` + +This query finds all desktop flows that include the terms `password` or `pwd`. + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6 + AND w.definition IS NOT NULL + AND (LOWER(w.definition) LIKE '%password%' OR LOWER(w.definition) LIKE '%pwd%'); +``` + +#### Data exfiltration risk + +This query identifies desktop flows that incorporate scripting actions to access SAP's internal GUI scripting engine. This can present potential data loss risks because sensitive data accessed or manipulated by these scripts could be unintentionally or maliciously exposed. + +```sql + SELECT + workflowid AS 'Desktop flow Id', + name AS 'Desktop flow', + definition AS 'Script' + FROM + workflow + WHERE + (definition LIKE '%System.RunVBScript%' + OR definition LIKE '%System.RunPowershellScript%' + OR definition LIKE '%System.RunJavascript%' + OR definition LIKE '%System.RunDotNetScript%' + OR definition LIKE '%System.RunPythonScript%') + AND definition LIKE '%SapGuiAuto.GetScriptingEngine%' + ORDER BY + workflowid; +``` + +### Potential SQL injection risk + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6 + AND w.definition IS NOT NULL + AND LOWER(w.definition) LIKE '%database.executesqlstatement%'; + +``` + +### Advance API request usage + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6 + AND w.definition IS NOT NULL + AND ( + LOWER(w.definition) LIKE '%curl%' OR + LOWER(w.definition) LIKE '%invoke-restmethod%' OR + LOWER(w.definition) LIKE '%invoke-webrequest%' OR + LOWER(w.definition) LIKE '%httpclient%' OR + LOWER(w.definition) LIKE '%requests.get%' OR + LOWER(w.definition) LIKE '%requests.post%' OR + LOWER(w.definition) LIKE '%fetch%' OR + LOWER(w.definition) LIKE '%axios%' OR + LOWER(w.definition) LIKE '%.ajax%' + ); + +``` + +### Web endpoints and URL shortcuts usage + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6 + AND w.definition IS NOT NULL + AND ( + LOWER(w.definition) LIKE '%bit.ly%' OR + LOWER(w.definition) LIKE '%linkedin.com%' OR + LOWER(w.definition) LIKE '%aka.ms%' OR + LOWER(w.definition) LIKE '%tinyurl.com%' OR + LOWER(w.definition) LIKE '%goo.gl%' OR + LOWER(w.definition) LIKE '%t.co%' OR + LOWER(w.definition) LIKE '%fb.me%' OR + LOWER(w.definition) LIKE '%is.gd%' OR + LOWER(w.definition) LIKE '%buff.ly%' + ); + +``` + +### Hardcode file path usage + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6 + AND w.definition IS NOT NULL + AND ( + LOWER(w.definition) LIKE '%c:\\%' OR + LOWER(w.definition) LIKE '%d:\\%' OR + LOWER(w.definition) LIKE '%e:\\%' OR + LOWER(w.definition) LIKE '%f:\\%' OR + LOWER(w.definition) LIKE '%g:\\%' OR + LOWER(w.definition) LIKE '%h:\\%' OR + LOWER(w.definition) LIKE '%i:\\%' OR + LOWER(w.definition) LIKE '%j:\\%' OR + LOWER(w.definition) LIKE '%k:\\%' OR + LOWER(w.definition) LIKE '%l:\\%' OR + LOWER(w.definition) LIKE '%m:\\%' OR + LOWER(w.definition) LIKE '%n:\\%' OR + LOWER(w.definition) LIKE '%o:\\%' OR + LOWER(w.definition) LIKE '%p:\\%' OR + LOWER(w.definition) LIKE '%q:\\%' OR + LOWER(w.definition) LIKE '%r:\\%' OR + LOWER(w.definition) LIKE '%s:\\%' OR + LOWER(w.definition) LIKE '%t:\\%' OR + LOWER(w.definition) LIKE '%u:\\%' OR + LOWER(w.definition) LIKE '%v:\\%' OR + LOWER(w.definition) LIKE '%w:\\%' OR + LOWER(w.definition) LIKE '%x:\\%' OR + LOWER(w.definition) LIKE '%y:\\%' OR + LOWER(w.definition) LIKE '%z:\\%' + ); + +``` + +### Missing error handling + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6 + AND w.definition IS NOT NULL + AND NOT (LOWER(w.definition) LIKE '%on block error%' OR LOWER(w.definition) LIKE '%on error%'); + +``` + ## Creating reports in Fabric ### Creating real-time alerts diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png 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insertions(+), 15 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index d8da42e1a..5996383d2 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -57,7 +57,7 @@ Before you continue, ensure you meet the following prerequisites: 5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of Fabric workspace showing the settings panel for a lakehouse with description." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: 6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#desktop-flow-action-log-level-analytics), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## List of tables are automation-related @@ -174,6 +174,7 @@ This query retrieves the minimum, mean (average), maximum, and standard deviatio ### Capacity and bottleneck-related query +This query identifies capacity and bottleneck issues in machines running a specific desktop flow to help in optimizing resource allocation and addressing performance constraints. ```sql SELECT @@ -206,9 +207,9 @@ This query retrieves the minimum, mean (average), maximum, and standard deviatio ``` -### Governance-related query examples +### Governance-related query examples for desktop flows -### Base desktop flow query with owner info +#### Base desktop flow query with owner info This query returns all desktop flows with their owner information. @@ -230,7 +231,7 @@ This query returns all desktop flows with their owner information. w.category = 6; -- Only consider desktop flows (category 6) ``` -### Find scripts that include the term `password` or `pwd` +#### Find scripts that include the term `password` or `pwd` This query finds all desktop flows that include the terms `password` or `pwd`. @@ -276,7 +277,9 @@ This query identifies desktop flows that incorporate scripting actions to access workflowid; ``` -### Potential SQL injection risk +#### Potential SQL injection risk + +This query identifies desktop flows that include scripts with potential SQL injection risks by searching for the use of `database.executesqlstatement` within the flow definitions. ```sql SELECT @@ -299,7 +302,9 @@ This query identifies desktop flows that incorporate scripting actions to access ``` -### Advance API request usage +#### Advance API request usage + +This query retrieves desktop flows that utilize advanced API request methods, such as `curl`, `Invoke-RestMethod`, and other `requests`, to identify connectivity to external web services or services. ```sql SELECT @@ -332,7 +337,9 @@ This query identifies desktop flows that incorporate scripting actions to access ``` -### Web endpoints and URL shortcuts usage +#### Web endpoints and URL shortcuts usage + +This query detects desktop flows containing scripts that reference URL shorteners to assess potential risks of restricted URL usage. ```sql SELECT @@ -365,7 +372,9 @@ This query identifies desktop flows that incorporate scripting actions to access ``` -### Hardcode file path usage +#### Hardcode file path usage + +This query identifies desktop flows that include hardcoded file paths in their scripts, which may indicate poor scripting best-practices and potential security vulnerabilities. ```sql SELECT @@ -413,7 +422,9 @@ This query identifies desktop flows that incorporate scripting actions to access ``` -### Missing error handling +#### Missing error handling + +This query searches for desktop flows that lack any error handling mechanisms, such as on `block error` or `on error`, to ensure robustness and reliability in script execution. ```sql SELECT @@ -436,6 +447,48 @@ This query identifies desktop flows that incorporate scripting actions to access ``` + +### Governance-related query examples for desktop flow run action logs + +> [!NOTE] +> +> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. + +#### Identify restricted URL usage + +This query finds web service invocations (Invoke Web Service action) within a specific desktop flow over the past three weeks. This is particularly useful for identifying and analyzing potentially suspicious endpoints or restricted API calls. + +```sql + SELECT + JSON_VALUE(f.data, '$.name') AS ActionName, + f.data AS 'Action log', + f.parentobjectid AS 'Parent object id', + f.createdon AS 'Log created on', + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + [flowlog] f + JOIN flowsession fs ON f.parentobjectid = fs.flowsessionid + JOIN workflow w ON fs.regardingobjectid = w.workflowid + JOIN systemuser s ON w.ownerid = s.systemuserid + WHERE + w.workflowid = '[specific_flow_id]' -- Replace with the actual flow ID + AND f.createdon >= DATEADD(day, -21, GETDATE()) + AND JSON_VALUE(f.data, '$.name') = 'Invoke web service' + AND ( + f.data LIKE '%contoso-default.crm.dynamics.com/api%' + OR f.data LIKE '%api.second-restricted-url.net%' + OR f.data LIKE '%api.third-restricted-url.de%' + OR f.data LIKE '%api.phishing-example.com%' + ); +``` + ## Creating reports in Fabric ### Creating real-time alerts @@ -444,10 +497,4 @@ This query identifies desktop flows that incorporate scripting actions to access ### Creating a Dataflow Gen2 to ingest data to a warehouse -### Desktop flow action log-level analytics - -> [!NOTE] -> -> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. - ## Closing From 6b59a62048c70f323e506cd0fb7f5d76d7a7f7cb Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 07:09:14 +0100 Subject: [PATCH 11/44] Next set of updates --- ...tion-related-data-analytics-with-fabric.md | 118 +++++++----------- 1 file changed, 47 insertions(+), 71 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 5996383d2..d70fd5984 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -18,7 +18,7 @@ search.audienceType: # Automation-related data analytics with Fabric -Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](/articles/automation-center-overview.md) and [Desktop Flow Activity](/articles/desktop-flows/desktop-flow-activity.md). +Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](./automation-center-overview.md) and [Desktop Flow Activity](./desktop-flows/desktop-flow-activity.md). However, your organization may have more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable, and compliant analytics solutions using data from Power Automate and many other sources. @@ -41,10 +41,6 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy In the following guide, we'll walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you'll have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. -> [!NOTE] -> -> **Disclaimer:** The scenarios, code examples, and data used in this tutorial are fictional, may include errors and intended solely for demonstration purposes. They do not represent product or analysis recommendations. - ## Prerequisites Before you continue, ensure you meet the following prerequisites: @@ -57,7 +53,7 @@ Before you continue, ensure you meet the following prerequisites: 5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of Fabric workspace showing the settings panel for a lakehouse with description." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: 6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## List of tables are automation-related @@ -68,7 +64,7 @@ The following table includes automation-related tables that are frequently used | [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | | [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | | [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | -| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | +| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | | [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | @@ -83,6 +79,10 @@ The image includes only relations to tables that are included above and automati ## Creating automation-related queries +> [!NOTE] +> +> **Disclaimer:** The scenarios, query examples, and data used in this tutorial are fictional, may include errors, inefficiencies and are intended solely for demonstration purposes. + Step-by-step instructions to create a sample SQL Query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. 1. Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com) and log in with your credentials. @@ -172,9 +172,9 @@ This query retrieves the minimum, mean (average), maximum, and standard deviatio :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png"::: -### Capacity and bottleneck-related query +### Machine and licensing capacity-related query -This query identifies capacity and bottleneck issues in machines running a specific desktop flow to help in optimizing resource allocation and addressing performance constraints. +This query identifies machine and licensing-related capacity issues for a specific desktop flow to help in optimizing resource allocation and addressing performance constraints. ```sql SELECT @@ -207,9 +207,34 @@ This query identifies capacity and bottleneck issues in machines running a speci ``` -### Governance-related query examples for desktop flows +### Basic flow queries + +#### Retrieve cloud flows with their owner info + +This query returns all cloud flows with their owner information. + +> [!NOTE] +> Only cloud flows that are part of a Dataverse solution are available in Fabric. + +```sql + SELECT + w.name AS 'Cloud flow', + w.workflowid AS 'Cloud flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.clientdata AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 5; -- Only consider solution-cloud flows (category 5) +``` -#### Base desktop flow query with owner info +#### Retrieve desktop flows with their owner info This query returns all desktop flows with their owner information. @@ -231,9 +256,11 @@ This query returns all desktop flows with their owner information. w.category = 6; -- Only consider desktop flows (category 6) ``` -#### Find scripts that include the term `password` or `pwd` +### Governance-related query examples for desktop flows + +#### Find scripts that include plain text passwords in connections -This query finds all desktop flows that include the terms `password` or `pwd`. +This query finds all desktop flows that use the OLEDB connections action with a plaintext password. ```sql SELECT @@ -252,12 +279,12 @@ This query finds all desktop flows that include the terms `password` or `pwd`. WHERE w.category = 6 AND w.definition IS NOT NULL - AND (LOWER(w.definition) LIKE '%password%' OR LOWER(w.definition) LIKE '%pwd%'); + AND (LOWER(w.definition) LIKE '%;password=%'); ``` -#### Data exfiltration risk +#### Potential sensitive data loss or exfiltration risk -This query identifies desktop flows that incorporate scripting actions to access SAP's internal GUI scripting engine. This can present potential data loss risks because sensitive data accessed or manipulated by these scripts could be unintentionally or maliciously exposed. +This query detects desktop flows that include scripting actions leveraging SAP's internal GUI scripting engine. If this is unexpected, it may pose risks of data loss or exfiltration, as sensitive information could be accessed or manipulated by these scripts and unintentionally or maliciously altered or exposed. ```sql SELECT @@ -279,7 +306,7 @@ This query identifies desktop flows that incorporate scripting actions to access #### Potential SQL injection risk -This query identifies desktop flows that include scripts with potential SQL injection risks by searching for the use of `database.executesqlstatement` within the flow definitions. +This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](./desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (e.g., *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. ```sql SELECT @@ -298,7 +325,7 @@ This query identifies desktop flows that include scripts with potential SQL inje WHERE w.category = 6 AND w.definition IS NOT NULL - AND LOWER(w.definition) LIKE '%database.executesqlstatement%'; + AND LOWER(w.definition) LIKE '%database.executesqlstatement.execute%'; ``` @@ -372,57 +399,7 @@ This query detects desktop flows containing scripts that reference URL shortener ``` -#### Hardcode file path usage - -This query identifies desktop flows that include hardcoded file paths in their scripts, which may indicate poor scripting best-practices and potential security vulnerabilities. - -```sql - SELECT - w.name AS 'Desktop flow', - w.workflowid AS 'Desktop flow Id', - w.createdon AS 'Created on', - w.modifiedon AS 'Last modified on', - w.definition AS 'Script', - w.ownerid AS 'Owner Id', - s.fullname AS 'Owner name', - s.internalemailaddress AS 'Owner email' - FROM - workflow w - JOIN - systemuser s ON w.ownerid = s.systemuserid - WHERE - w.category = 6 - AND w.definition IS NOT NULL - AND ( - LOWER(w.definition) LIKE '%c:\\%' OR - LOWER(w.definition) LIKE '%d:\\%' OR - LOWER(w.definition) LIKE '%e:\\%' OR - LOWER(w.definition) LIKE '%f:\\%' OR - LOWER(w.definition) LIKE '%g:\\%' OR - LOWER(w.definition) LIKE '%h:\\%' OR - LOWER(w.definition) LIKE '%i:\\%' OR - LOWER(w.definition) LIKE '%j:\\%' OR - LOWER(w.definition) LIKE '%k:\\%' OR - LOWER(w.definition) LIKE '%l:\\%' OR - LOWER(w.definition) LIKE '%m:\\%' OR - LOWER(w.definition) LIKE '%n:\\%' OR - LOWER(w.definition) LIKE '%o:\\%' OR - LOWER(w.definition) LIKE '%p:\\%' OR - LOWER(w.definition) LIKE '%q:\\%' OR - LOWER(w.definition) LIKE '%r:\\%' OR - LOWER(w.definition) LIKE '%s:\\%' OR - LOWER(w.definition) LIKE '%t:\\%' OR - LOWER(w.definition) LIKE '%u:\\%' OR - LOWER(w.definition) LIKE '%v:\\%' OR - LOWER(w.definition) LIKE '%w:\\%' OR - LOWER(w.definition) LIKE '%x:\\%' OR - LOWER(w.definition) LIKE '%y:\\%' OR - LOWER(w.definition) LIKE '%z:\\%' - ); - -``` - -#### Missing error handling +#### Missing error handling in scripts This query searches for desktop flows that lack any error handling mechanisms, such as on `block error` or `on error`, to ensure robustness and reliability in script execution. @@ -447,12 +424,11 @@ This query searches for desktop flows that lack any error handling mechanisms, s ``` - ### Governance-related query examples for desktop flow run action logs > [!NOTE] > -> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/articles/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. +> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. #### Identify restricted URL usage From 87c4d95e14ef24ba3a78d14ea8eb9f4bf5d0a6c9 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 07:14:18 +0100 Subject: [PATCH 12/44] Update advanced-automation-related-data-analytics-with-fabric.md --- ...tion-related-data-analytics-with-fabric.md | 100 +++++++++--------- 1 file changed, 50 insertions(+), 50 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index d70fd5984..0bb755423 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -55,7 +55,7 @@ Before you continue, ensure you meet the following prerequisites: 6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. 7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. -## List of tables are automation-related +## List of automation-related tables The following table includes automation-related tables that are frequently used for reporting and observability use cases. @@ -137,6 +137,55 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: 8. (Optional) To store the SQL query for future use, click on the "Save Query" button. +### Basic flow queries + +#### Retrieve cloud flows with their owner info + +This query returns all cloud flows with their owner information. + +> [!NOTE] +> Only cloud flows that are part of a Dataverse solution are available in Fabric. + +```sql + SELECT + w.name AS 'Cloud flow', + w.workflowid AS 'Cloud flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.clientdata AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 5; -- Only consider solution-cloud flows (category 5) +``` + +#### Retrieve desktop flows with their owner info + +This query returns all desktop flows with their owner information. + +```sql + SELECT + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM + workflow w + JOIN + systemuser s ON w.ownerid = s.systemuserid + WHERE + w.category = 6; -- Only consider desktop flows (category 6) +``` + ### Performance-related query example This query retrieves the minimum, mean (average), maximum, and standard deviation of runtimes for desktop flow runs (Flow Sessions) of a specified desktop flow, with the runtimes converted from milliseconds rounded up to the nearest full second. The results are grouped by machine IDs and include additional details such as machine names, management types, maximum hosted machine counts, session capacity and the last heartbeat date etc. from the Machine Group and Machine tables. @@ -207,55 +256,6 @@ This query identifies machine and licensing-related capacity issues for a specif ``` -### Basic flow queries - -#### Retrieve cloud flows with their owner info - -This query returns all cloud flows with their owner information. - -> [!NOTE] -> Only cloud flows that are part of a Dataverse solution are available in Fabric. - -```sql - SELECT - w.name AS 'Cloud flow', - w.workflowid AS 'Cloud flow Id', - w.createdon AS 'Created on', - w.modifiedon AS 'Last modified on', - w.clientdata AS 'Script', - w.ownerid AS 'Owner Id', - s.fullname AS 'Owner name', - s.internalemailaddress AS 'Owner email' - FROM - workflow w - JOIN - systemuser s ON w.ownerid = s.systemuserid - WHERE - w.category = 5; -- Only consider solution-cloud flows (category 5) -``` - -#### Retrieve desktop flows with their owner info - -This query returns all desktop flows with their owner information. - -```sql - SELECT - w.name AS 'Desktop flow', - w.workflowid AS 'Desktop flow Id', - w.createdon AS 'Created on', - w.modifiedon AS 'Last modified on', - w.definition AS 'Script', - w.ownerid AS 'Owner Id', - s.fullname AS 'Owner name', - s.internalemailaddress AS 'Owner email' - FROM - workflow w - JOIN - systemuser s ON w.ownerid = s.systemuserid - WHERE - w.category = 6; -- Only consider desktop flows (category 6) -``` - ### Governance-related query examples for desktop flows #### Find scripts that include plain text passwords in connections From 5655e94d75c0febe1d6219363f14e71135f43174 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 07:23:22 +0100 Subject: [PATCH 13/44] Fixes --- ...mation-related-data-analytics-with-fabric.md | 6 ++++-- .../execute-sql-statmenet-risk.png | Bin 0 -> 30588 bytes 2 files changed, 4 insertions(+), 2 deletions(-) create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 0bb755423..dcd30ec62 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -141,7 +141,7 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End #### Retrieve cloud flows with their owner info -This query returns all cloud flows with their owner information. +This query returns all cloud flows with their owner information. > [!NOTE] > Only cloud flows that are part of a Dataverse solution are available in Fabric. @@ -260,7 +260,7 @@ This query identifies machine and licensing-related capacity issues for a specif #### Find scripts that include plain text passwords in connections -This query finds all desktop flows that use the OLEDB connections action with a plaintext password. +This query finds all desktop flows that use (OLEDB) DB connection strings that have been configured to use a plaintext password. ```sql SELECT @@ -308,6 +308,8 @@ This query detects desktop flows that include scripting actions leveraging SAP's This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](./desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (e.g., *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: + ```sql SELECT w.name AS 'Desktop flow', diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png b/articles/media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png new file mode 100644 index 0000000000000000000000000000000000000000..8d14ef21eb43016f09315d6ca7259d2526d699a0 GIT binary patch literal 30588 zcmdqJby$^Mw>P>#0cin|25F?bL+LIl>F(|ZrCUNkQbIvGq`Rd>x>LHlIdl0u&%3Yl ze&4sxK6{_P4%Y>&b>DN%d&Zb!{Kgo+vBDJPB~X#@ksuHVs+6RtG6Vu^4Sofmz=JC= zqiWOOAELeFTPFwvtrPkS^N|jX09-_HmXZ@g*hEHx$D;bwrn&Lwl9-0`YZqs8J4eX3 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z{{d1fM3@~rj}T>XDo{6cF*7~VAZt`t`^PcUAq9flu9!LQdJ$=Hjk4>%g;N);I2NGf zq9SEqwU%iM>5-xvdEYxW0ymEuf3c5=KcwX~Y~XI*Il`Zj+1X&c-TMuUu?)+r{e*@H zPF=AAP9z)T+>P`G!TS;e@M(-hOA-`OEu{^~B1yZb&9C8>Z%4OLl3LS%+MZ+evb3iA zw-yTSA4jB~U)W11A*?IUzApG<$*cd7=k+u7fLtXc{{d%58ENEyn~H|Cz@4%ooOr$L z)aISPLH7HooIpx_yvINrbyoK0RaT(M2~m16UE)_5QC`ia&z4BWjBb^V0d)EirS z`+OT=24c?KZbT|Ay)c6OaBraDReb z`}-@*g#JQUzi1|9-~01P2i$R;VeMdJW8)8LxB$j*BOnHlE~C;Iz!2x7#`I#c*0S0Y z>+0&7sG5#}z6O3ui`u15c{yOiI2|*v6b-z17%JLa38n_^cO41xF;Uh z&tt}GQahgLL93*lhi8sG6Nu6T3$xGcF}FM)b-e)(3;Ljl4rQ@!-_Og8y!NS319tmp yvHH#r9|B|PdRwuv^mzyBAKStIQL literal 0 HcmV?d00001 From 4dc84bfa5d9458a643b276897e2bbb858c69e083 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 07:26:03 +0100 Subject: [PATCH 14/44] Update advanced-automation-related-data-analytics-with-fabric.md --- ...ed-automation-related-data-analytics-with-fabric.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index dcd30ec62..a5a75e107 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -18,7 +18,7 @@ search.audienceType: # Automation-related data analytics with Fabric -Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](./automation-center-overview.md) and [Desktop Flow Activity](./desktop-flows/desktop-flow-activity.md). +Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](./automation-center-overview.md) and [Desktop Flow Activity](/desktop-flows/desktop-flow-activity.md). However, your organization may have more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable, and compliant analytics solutions using data from Power Automate and many other sources. @@ -53,7 +53,7 @@ Before you continue, ensure you meet the following prerequisites: 5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of Fabric workspace showing the settings panel for a lakehouse with description." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: 6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## List of automation-related tables @@ -64,7 +64,7 @@ The following table includes automation-related tables that are frequently used | [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | | [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | | [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | -| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | +| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](/desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | | [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | @@ -306,7 +306,7 @@ This query detects desktop flows that include scripting actions leveraging SAP's #### Potential SQL injection risk -This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](./desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (e.g., *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. +This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (e.g., *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: @@ -430,7 +430,7 @@ This query searches for desktop flows that lack any error handling mechanisms, s > [!NOTE] > -> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. +> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. #### Identify restricted URL usage From 9adb4f258308ab04908504aee38f8707c8dfe0fa Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 07:46:15 +0100 Subject: [PATCH 15/44] added image alt-text --- ...tion-related-data-analytics-with-fabric.md | 22 +++++++++--------- .../automation-related-table-relations.png | Bin 78739 -> 80666 bytes 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index a5a75e107..15359339a 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -18,7 +18,7 @@ search.audienceType: # Automation-related data analytics with Fabric -Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](./automation-center-overview.md) and [Desktop Flow Activity](/desktop-flows/desktop-flow-activity.md). +Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](automation-center-overview.md) and [Desktop Flow Activity](./desktop-flows/desktop-flow-activity.md). However, your organization may have more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable, and compliant analytics solutions using data from Power Automate and many other sources. @@ -26,7 +26,7 @@ However, your organization may have more advanced or custom monitoring, reportin [Microsoft Fabric](/fabric/get-started/microsoft-fabric-overview) is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a unified suite of analytical services including Data Engineering, Data Science, Real-Time Analytics, Data Warehousing with advanced data governance and security control. -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Infographic showing different components of the Fabric data platform. It includes sections for Data Integration (Data Factory), Data Engineering (Synapse), Data Warehouse (Synapse), Data Science (Synapse), Real-Time Analytics (Synapse), Applied Observability (Data Activator), and Business Intelligence (Power BI)." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: ## Dataverse direct link to Microsoft Fabric @@ -51,9 +51,9 @@ Before you continue, ensure you meet the following prerequisites: 4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you will see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "dataverse," followed by your environment name and a unique environment suffix, such as ` dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: 5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of Fabric workspace showing the settings panel for a lakehouse with description." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: 6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## List of automation-related tables @@ -64,7 +64,7 @@ The following table includes automation-related tables that are frequently used | [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | | [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | | [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | -| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](/desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | +| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | | [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | @@ -132,9 +132,9 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End | Ignored | 12 | 6. Review the query results to ensure they meet your requirements. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of a SQL query being executed in a database management tool." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: 7. (Optional) You can open a Live-query with results in Excel by highlighting the SQL query and selecting "Open in Excel" in the query output section. This will generate and download an Excel file with a Live-query to the SQL Analytics endpoint to further deep-dive on the results. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of a SQL query being executed in a database query panel in Farbic." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: 8. (Optional) To store the SQL query for future use, click on the "Save Query" button. ### Basic flow queries @@ -219,7 +219,7 @@ This query retrieves the minimum, mean (average), maximum, and standard deviatio mean_runtime DESC; ``` -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png"::: +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png" alt-text="Screenshot of a SQL query being executed in Fabric with a results window." lightbox="media/advanced-automation-related-data-analytics-fabric/performance-related-query-results.png"::: ### Machine and licensing capacity-related query @@ -306,9 +306,9 @@ This query detects desktop flows that include scripting actions leveraging SAP's #### Potential SQL injection risk -This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (e.g., *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. +This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](./desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (e.g., *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an 'Execute SQL statement' configuration dialog in Power Automate desktop." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: ```sql SELECT @@ -430,7 +430,7 @@ This query searches for desktop flows that lack any error handling mechanisms, s > [!NOTE] > -> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. +> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. #### Identify restricted URL usage diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png b/articles/media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png index a943bd231cfdb79422443c5718532d1d06a1fbd1..2c6e87955181e1427ea7628c3eef209bd2d03090 100644 GIT binary patch literal 80666 zcmbUJ1yojT_dbd)1Qit!1d)!1mhLWTX;4(Um6q-hR2rmF2}PtsT3V3~QBq2j5b01r zde6oC`<-*fIAiSb-k>VnPAowcE@;V5DZ4UoQ z;+}&~jy*rTga5nmQ2CxKf)KQ#|6wJu6Hvm3=iOA)70&;@bnzTLUHU-bo70aJ?z+jk zyFGAnLGnv_mEj{iH~2{2%GJWf=AoO7lOsZWft~?AzleS==k(Cq#peD4H>ButuNZtv 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zuQlbDD=New7*R>r2RDNBTer@syNHt2!(apPSXB{&3|?Ofbv2DaEP})fzdt(O5h(04 z-QdJc4^+OjO~3JMH;W`Xe+;j84uHUQk*XhM=tWqFoI~9|kDmfHPf>V4$9FB`*)v&T zi`AQP^kJ679j9uc4`~euTOOIPyiKef~LZEi^!)O=L7GhsZy2~2uqTGyB?Q^Zss^@pvG4hni`sxG(0TQ-4})ta;` zM<>up1dsZpUoWCJ+T;Iu17e^&xr%A+9LE>d~~1YD!rRO^wM From c9b11417d263713395bf83c9816c14de75ba6d9c Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 11:35:32 +0100 Subject: [PATCH 16/44] Update advanced-automation-related-data-analytics-with-fabric.md --- ...tion-related-data-analytics-with-fabric.md | 131 +++++++++++++++++- 1 file changed, 129 insertions(+), 2 deletions(-) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/advanced-automation-related-data-analytics-with-fabric.md index 15359339a..51de2b77c 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/advanced-automation-related-data-analytics-with-fabric.md @@ -426,13 +426,13 @@ This query searches for desktop flows that lack any error handling mechanisms, s ``` -### Governance-related query examples for desktop flow run action logs +### Governance-related query examples for V2 action logs > [!NOTE] > > Before you continue with this section, ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. -#### Identify restricted URL usage +#### Desktop flow runs with restricted URL access This query finds web service invocations (Invoke Web Service action) within a specific desktop flow over the past three weeks. This is particularly useful for identifying and analyzing potentially suspicious endpoints or restricted API calls. @@ -467,6 +467,133 @@ This query finds web service invocations (Invoke Web Service action) within a sp ); ``` +#### Desktop flow runs with cryprtographic code + +This query scans desktop flow runs for PowerShell script actions that included cryptographic code over the past 7 days. + +```sql + -- Queries actions logs named 'Run PowerShell script' that contain code that that uses cryptographic libraries + -- and terms such as "AES", "RSA", "encryption", or "decryption," which may indicate risky operations + SELECT top(1) + JSON_VALUE(data, '$.name') AS ActionName, + JSON_VALUE(data, '$.inputs') AS Inputs, + JSON_VALUE(data, '$.outputs') AS Outputs + FROM + [flowlog] + WHERE + JSON_VALUE(data, '$.name') = 'Run PowerShell script' + AND createdon >= DATEADD(day, -7, GETDATE()) + AND ( + JSON_VALUE(data, '$.inputs') LIKE '%AES%' + OR JSON_VALUE(data, '$.inputs') LIKE '%RSA%' + OR JSON_VALUE(data, '$.inputs') LIKE '%encryption%' + OR JSON_VALUE(data, '$.inputs') LIKE '%decryption%' + ) + ORDER BY + ActionName + +``` + +#### Desktop flow runs with pro-code usage + +This query is a bit more advanced. It identifies and counts the number of distinct desktop flow runs (Flow Sessions) that include pro-coding parts (such as VBScript, PowerShell, JavaScript, .NET, or Python) within the last 7 days, and then groups the results bydesktop flow. + +```sql +WITH ProCodingSessions AS ( + SELECT + fs.flowsessionid, + f.data AS 'Action log', + f.parentobjectid AS 'Parent object id', + f.createdon AS 'Log created on', + w.name AS 'Desktop flow', + w.workflowid AS 'Desktop flow Id', + w.createdon AS 'Created on', + w.modifiedon AS 'Last modified on', + w.definition AS 'Script', + w.ownerid AS 'Owner Id', + s.fullname AS 'Owner name', + s.internalemailaddress AS 'Owner email' + FROM [flowlog] f + JOIN flowsession fs ON f.parentobjectid = fs.flowsessionid + JOIN workflow w ON fs.regardingobjectid = w.workflowid + JOIN systemuser s ON w.ownerid = s.systemuserid + WHERE f.createdon >= DATEADD(day, -7, GETDATE()) + AND ( + LOWER(w.definition) LIKE '%runvbscript%' OR + LOWER(w.definition) LIKE '%runpowershellscript%' OR + LOWER(w.definition) LIKE '%runjavascript%' OR + LOWER(w.definition) LIKE '%rundotnetscript%' OR + LOWER(w.definition) LIKE '%runpythonscript%' + ) +), +FlowCounts AS ( + SELECT + p.[Desktop flow], + p.[Desktop flow Id], + p.[Created on], + p.[Last modified on], + p.[Script], + p.[Owner Id], + p.[Owner name], + p.[Owner email], + COUNT(DISTINCT p.flowsessionid) AS ProCodingSessionCount + FROM ProCodingSessions p + GROUP BY + p.[Desktop flow], + p.[Desktop flow Id], + p.[Created on], + p.[Last modified on], + p.[Script], + p.[Owner Id], + p.[Owner name], + p.[Owner email] +) +SELECT + f.[Desktop flow], + f.[Desktop flow Id], + f.[Created on], + f.[Last modified on], + f.[Script], + f.[Owner Id], + f.[Owner name], + f.[Owner email], + f.ProCodingSessionCount AS 'Runs with pro-code' +FROM FlowCounts f +ORDER BY f.ProCodingSessionCount DESC; + +``` + +### Error and perfromance-related queries for V2 action logs + +#### Top 10 failing desktop flow actions + +This query returns the top 10 failing actions by number of errors over the past 7 days. + +```sql + SELECT TOP(10) + JSON_VALUE(data, '$.name') AS ActionName, + SUM(CASE WHEN JSON_VALUE(data, '$.status') = 'Failed' THEN 1 ELSE 0 END) AS ErrorCount + FROM [flowlog] + WHERE createdon >= DATEADD(day, -7, GETDATE()) + GROUP BY JSON_VALUE(data, '$.name') + HAVING SUM(CASE WHEN JSON_VALUE(data, '$.status') = 'Failed' THEN 1 ELSE 0 END) > 0 + ORDER BY ErrorCount DESC; +``` + +#### Top 10 error codes with count + +```sql + SELECT TOP(10) + JSON_VALUE(data, '$.errorCode') AS ErrorCode, + COUNT(*) AS OccurrenceCount + FROM [flowlog] + WHERE createdon >= DATEADD(day, -7, GETDATE()) + AND JSON_VALUE(data, '$.status') = 'Failed' + GROUP BY JSON_VALUE(data, '$.errorCode') + ORDER BY OccurrenceCount DESC; + +``` + ## Creating reports in Fabric ### Creating real-time alerts From 1ce742f993fb910eb8a80e7dc32a22d584f71c42 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 15:06:58 +0100 Subject: [PATCH 17/44] New page structure --- articles/TOC.yml | 16 +++- ...automation-analytics-with-fabric-alerts.md | 26 +++++++ ...tion-analytics-with-fabric-introduction.md | 68 ++++++++++++++++ ...tomation-analytics-with-fabric-queries.md} | 78 +++---------------- ...utomation-analytics-with-fabric-reports.md | 32 ++++++++ ...omation-analytics-with-fabric-warehouse.md | 31 ++++++++ 6 files changed, 181 insertions(+), 70 deletions(-) create mode 100644 articles/automation-analytics-with-fabric-alerts.md create mode 100644 articles/automation-analytics-with-fabric-introduction.md rename articles/{advanced-automation-related-data-analytics-with-fabric.md => automation-analytics-with-fabric-queries.md} (69%) create mode 100644 articles/automation-analytics-with-fabric-reports.md create mode 100644 articles/automation-analytics-with-fabric-warehouse.md diff --git a/articles/TOC.yml b/articles/TOC.yml index 79f1c0ea3..6d2accfbc 100644 --- a/articles/TOC.yml +++ b/articles/TOC.yml @@ -1116,11 +1116,23 @@ - name: Automation center (preview) items: - name: Overview - href: automation-center-overview.md + href: automation-analytics-with-fabric-introduction.md + - name: Crafting automation-related queries with Fabric + href: automation-analytics-with-fabric-queries.md + - name: Building custom Power BI reports for data insights in Fabric + href: automation-analytics-with-fabric-reports.md + - name: Designing an automation-centric Fabric warehouse + href: automation-analytics-with-fabric-warehouse.md + - name: Setting up real-time alerts with Reflex + href: automation-analytics-with-fabric-alerts.md + - name: Automation-centric analytics with Fabric + items: + - name: Overview + href: autom - name: Recommendations href: automation-center-recommendations.md - name: Copilot - href: automation-center-copilot.md + href: automation-center-copilot.md - name: Capacity utilization items: - name: Introduction to capacity utilization (preview) diff --git a/articles/automation-analytics-with-fabric-alerts.md b/articles/automation-analytics-with-fabric-alerts.md new file mode 100644 index 000000000..3d18c66a6 --- /dev/null +++ b/articles/automation-analytics-with-fabric-alerts.md @@ -0,0 +1,26 @@ +--- +title: Setting up real-time alerts with Reflex for proactive monitoring +description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. +ms.topic: conceptual +ms.date: 11/06/2024 +ms.author: appapaio +ms.reviewer: dmartens +contributors: + - DanaMartens +author: rpapostolis +ms.collection: conceptual +search.audienceType: + - admin + - coe + - flowmaker + - enduser +--- + +# Setting up real-time alerts with Reflex for proactive monitoring + +## Learn more + +- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) +- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) +- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) +- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md new file mode 100644 index 000000000..8432c5c3d --- /dev/null +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -0,0 +1,68 @@ +--- +title: Automation-centric data analytics with the Dataverse - Fabric integration +description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. +ms.topic: conceptual +ms.date: 11/06/2024 +ms.author: appapaio +ms.reviewer: dmartens +contributors: + - DanaMartens +author: rpapostolis +ms.collection: conceptual +search.audienceType: + - admin + - coe + - flowmaker + - enduser +--- + +# Automation-centric data analytics with Fabric + +Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](automation-center-overview.md) and [Desktop Flow Activity](./desktop-flows/desktop-flow-activity.md). + +However, your organization likely has more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable, and compliant analytics solutions using data from Power Automate and many other sources. + +## Microsoft Fabric as your analytics platform + +[Microsoft Fabric](/fabric/get-started/microsoft-fabric-overview) is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a unified suite of analytical services including Data Engineering, Data Science, Real-Time Analytics, Data Warehousing with advanced data governance and security control. + +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Infographic showing different components of the Fabric data platform. It includes sections for Data Integration (Data Factory), Data Engineering (Synapse), Data Warehouse (Synapse), Data Science (Synapse), Real-Time Analytics (Synapse), Applied Observability (Data Activator), and Business Intelligence (Power BI)." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: + +## Dataverse direct link to Microsoft Fabric + +The Link to Microsoft Fabric feature built into Power Platform makes all your Dynamics 365, Power Apps and Power Automate data available in [Microsoft OneLake](/fabric/onelake/onelake-overview), the built-in data lake for Microsoft Fabric. Key benefits of the Dataverse direct link: + +- No need to export data, build extract, transform, load (ETL) pipelines, or use our partner integration tools. +- With shortcuts from Dataverse directly into OneLake, your data stays in Dataverse while authorized users get to work with data in Fabric. +- Link data from all Dynamics 365 apps, including Dynamics 365 Finance and Operations apps. +- Build Power Apps and automations to drive action from insights in OneLake. + +## Get started + +In this guide, we'll walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you'll have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. + +## Prerequisites + +Before you continue, ensure you meet the following prerequisites: + +1. Have a basic understanding of data modeling, Power Automate, Dataverse, and the Fabric ecosystem. +2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. +3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. +4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you will see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: +5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: +6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. + +## How to get started + +> [!div class="nextstepaction"] +> [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) + +## Learn more + +- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) +- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) +- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) +- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) diff --git a/articles/advanced-automation-related-data-analytics-with-fabric.md b/articles/automation-analytics-with-fabric-queries.md similarity index 69% rename from articles/advanced-automation-related-data-analytics-with-fabric.md rename to articles/automation-analytics-with-fabric-queries.md index 51de2b77c..9b05ad555 100644 --- a/articles/advanced-automation-related-data-analytics-with-fabric.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -1,5 +1,5 @@ --- -title: Automation-related data analytics with the Dataverse - Fabric integration +title: Building custom Power BI reports for data insights in Fabric description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. ms.topic: conceptual ms.date: 11/06/2024 @@ -16,68 +16,8 @@ search.audienceType: - enduser --- -# Automation-related data analytics with Fabric +## Building custom Power BI reports for data insights in Fabric -Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](automation-center-overview.md) and [Desktop Flow Activity](./desktop-flows/desktop-flow-activity.md). - -However, your organization may have more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable, and compliant analytics solutions using data from Power Automate and many other sources. - -## Microsoft Fabric as your analytics platform - -[Microsoft Fabric](/fabric/get-started/microsoft-fabric-overview) is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a unified suite of analytical services including Data Engineering, Data Science, Real-Time Analytics, Data Warehousing with advanced data governance and security control. - -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Infographic showing different components of the Fabric data platform. It includes sections for Data Integration (Data Factory), Data Engineering (Synapse), Data Warehouse (Synapse), Data Science (Synapse), Real-Time Analytics (Synapse), Applied Observability (Data Activator), and Business Intelligence (Power BI)." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: - -## Dataverse direct link to Microsoft Fabric - -The Link to Microsoft Fabric feature built into Power Platform makes all your Dynamics 365, Power Apps and Power Automate data available in [Microsoft OneLake](/fabric/onelake/onelake-overview), the built-in data lake for Microsoft Fabric. Key benefits of the Dataverse direct link: - -- No need to export data, build extract, transform, load (ETL) pipelines, or use our partner integration tools. -- With shortcuts from Dataverse directly into OneLake, your data stays in Dataverse while authorized users get to work with data in Fabric. -- Link data from all Dynamics 365 apps, including Dynamics 365 Finance and Operations apps. -- Build Power Apps and automations to drive action from insights in OneLake. - -## Get started - -In the following guide, we'll walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you'll have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. - -## Prerequisites - -Before you continue, ensure you meet the following prerequisites: - -1. Have a basic understanding of data modeling, Power Automate, Dataverse, and the Fabric ecosystem. -2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. -3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. -4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you will see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "dataverse," followed by your environment name and a unique environment suffix, such as ` dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: -5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: -6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. - -## List of automation-related tables - -The following table includes automation-related tables that are frequently used for reporting and observability use cases. - -| Display name | Object name | Purpose | -|--------------------|------------------|-------------------------------------------------------| -| [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | -| [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | -| [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | -| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | -| [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | -| [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | -| [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | -| [Work Queue Item](/power-apps/developer/data-platform/reference/entities/workqueueitem) | workqueueitem | Contains information about each run of a workflow.| -| [User](/power-apps/developer/data-platform/reference/entities/systemuser) | systemuser | Represents an instance of a workflow execution. | - -### Simplified table relationship diagram - -The image includes only relations to tables that are included above and automation-relevant. - -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png"::: - -## Creating automation-related queries > [!NOTE] > @@ -594,12 +534,14 @@ This query returns the top 10 failing actions by number of errors over the past ``` -## Creating reports in Fabric - -### Creating real-time alerts +## What's next -## Persisting telemetry data in a Fabric Warehouse +> [!div class="nextstepaction"] +> [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) -### Creating a Dataflow Gen2 to ingest data to a warehouse +## Learn more -## Closing +- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) +- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) +- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) +- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) \ No newline at end of file diff --git a/articles/automation-analytics-with-fabric-reports.md b/articles/automation-analytics-with-fabric-reports.md new file mode 100644 index 000000000..a3b08a924 --- /dev/null +++ b/articles/automation-analytics-with-fabric-reports.md @@ -0,0 +1,32 @@ +--- +title: Building custom Power BI reports for data insights in Fabric +description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. +ms.topic: conceptual +ms.date: 11/06/2024 +ms.author: appapaio +ms.reviewer: dmartens +contributors: + - DanaMartens +author: rpapostolis +ms.collection: conceptual +search.audienceType: + - admin + - coe + - flowmaker + - enduser +--- + +# Building custom Power BI reports for data insights in Fabric + + +## What's next + +> [!div class="nextstepaction"] +> [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) + +## Learn more + +- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) +- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) +- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) +- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) \ No newline at end of file diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md new file mode 100644 index 000000000..b9a37abef --- /dev/null +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -0,0 +1,31 @@ +--- +title: Designing an automation-centric Fabric warehouse +description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. +ms.topic: conceptual +ms.date: 11/06/2024 +ms.author: appapaio +ms.reviewer: dmartens +contributors: + - DanaMartens +author: rpapostolis +ms.collection: conceptual +search.audienceType: + - admin + - coe + - flowmaker + - enduser +--- + +# Designing an automation-centric Fabric warehouse + +## What's next + +> [!div class="nextstepaction"] +> [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) + +## Learn more + +- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) +- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) +- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) +- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) \ No newline at end of file From 269fd43e1e38ec2742e02319c696cbe702d6b738 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 15:14:47 +0100 Subject: [PATCH 18/44] Update TOC.yml --- articles/TOC.yml | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/articles/TOC.yml b/articles/TOC.yml index 6d2accfbc..475525cbd 100644 --- a/articles/TOC.yml +++ b/articles/TOC.yml @@ -1114,6 +1114,14 @@ - name: Manage automations items: - name: Automation center (preview) + items: + - name: Overview + href: automation-center-overview.md + - name: Recommendations + href: automation-center-recommendations.md + - name: Copilot + href: automation-center-copilot.md + - name: Automation-centric analytics with Fabric items: - name: Overview href: automation-analytics-with-fabric-introduction.md @@ -1124,15 +1132,7 @@ - name: Designing an automation-centric Fabric warehouse href: automation-analytics-with-fabric-warehouse.md - name: Setting up real-time alerts with Reflex - href: automation-analytics-with-fabric-alerts.md - - name: Automation-centric analytics with Fabric - items: - - name: Overview - href: autom - - name: Recommendations - href: automation-center-recommendations.md - - name: Copilot - href: automation-center-copilot.md + href: automation-analytics-with-fabric-alerts.md - name: Capacity utilization items: - name: Introduction to capacity utilization (preview) From 5662feddbc2c40b23e797efe19d6c00c1c2de3ed Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 15:18:20 +0100 Subject: [PATCH 19/44] Update TOC.yml --- articles/TOC.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/articles/TOC.yml b/articles/TOC.yml index 475525cbd..65f62ee1d 100644 --- a/articles/TOC.yml +++ b/articles/TOC.yml @@ -1125,9 +1125,9 @@ items: - name: Overview href: automation-analytics-with-fabric-introduction.md - - name: Crafting automation-related queries with Fabric + - name: Crafting SQL analytics queries with Fabric href: automation-analytics-with-fabric-queries.md - - name: Building custom Power BI reports for data insights in Fabric + - name: Building custom Power BI reports in Fabric href: automation-analytics-with-fabric-reports.md - name: Designing an automation-centric Fabric warehouse href: automation-analytics-with-fabric-warehouse.md From bfaea17aebd324beeed909548a135056492c6937 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 15:30:44 +0100 Subject: [PATCH 20/44] Smaller fixes --- articles/automation-analytics-with-fabric-introduction.md | 4 ++-- articles/automation-analytics-with-fabric-reports.md | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 8432c5c3d..dec5a5cd4 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -53,9 +53,9 @@ Before you continue, ensure you meet the following prerequisites: 5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: 6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](#governance-related-query-examples-for-desktop-flow-run-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. -## How to get started +## What's next > [!div class="nextstepaction"] > [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) diff --git a/articles/automation-analytics-with-fabric-reports.md b/articles/automation-analytics-with-fabric-reports.md index a3b08a924..37f7cccea 100644 --- a/articles/automation-analytics-with-fabric-reports.md +++ b/articles/automation-analytics-with-fabric-reports.md @@ -18,7 +18,6 @@ search.audienceType: # Building custom Power BI reports for data insights in Fabric - ## What's next > [!div class="nextstepaction"] From 39c4f65de5c6500b7b05fa433aa057c92e897bed Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Sun, 10 Nov 2024 15:32:40 +0100 Subject: [PATCH 21/44] Update automation-analytics-with-fabric-queries.md --- articles/automation-analytics-with-fabric-queries.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 9b05ad555..a08e8397c 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -1,5 +1,5 @@ --- -title: Building custom Power BI reports for data insights in Fabric +title: Crafting automation-related queries with Fabric description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. ms.topic: conceptual ms.date: 11/06/2024 @@ -16,7 +16,7 @@ search.audienceType: - enduser --- -## Building custom Power BI reports for data insights in Fabric +## Crafting automation-related queries with Fabric > [!NOTE] From 5b5d15383bed49517fb81289af13c5c1fe42fdfa Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 09:26:20 +0100 Subject: [PATCH 22/44] Warehousing added --- ...tion-analytics-with-fabric-introduction.md | 2 +- ...omation-analytics-with-fabric-warehouse.md | 239 ++++++++++++++++++ ...utomation-semantic-model-relationships.png | Bin 0 -> 240895 bytes ...mation-warehouse-table-creation-result.png | Bin 0 -> 326750 bytes .../manage-default-semantic-model.png | Bin 0 -> 406177 bytes .../mark-as-date-table-column-select.png | Bin 0 -> 277514 bytes .../mark-as-date-table.png | Bin 0 -> 274144 bytes .../new-semantic-model.png | Bin 0 -> 408362 bytes .../semantic-model-erd.png | Bin 0 -> 43092 bytes 9 files changed, 240 insertions(+), 1 deletion(-) create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/automation-warehouse-table-creation-result.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/mark-as-date-table-column-select.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/mark-as-date-table.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/new-semantic-model.png create mode 100644 articles/media/advanced-automation-related-data-analytics-fabric/semantic-model-erd.png diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index dec5a5cd4..631b0bc10 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -26,7 +26,7 @@ However, your organization likely has more advanced or custom monitoring, report [Microsoft Fabric](/fabric/get-started/microsoft-fabric-overview) is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a unified suite of analytical services including Data Engineering, Data Science, Real-Time Analytics, Data Warehousing with advanced data governance and security control. -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Infographic showing different components of the Fabric data platform. It includes sections for Data Integration (Data Factory), Data Engineering (Synapse), Data Warehouse (Synapse), Data Science (Synapse), Real-Time Analytics (Synapse), Applied Observability (Data Activator), and Business Intelligence (Power BI)." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Infographic showing different components of the Fabric data platform." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: ## Dataverse direct link to Microsoft Fabric diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index b9a37abef..760488f8c 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -18,6 +18,245 @@ search.audienceType: # Designing an automation-centric Fabric warehouse +There are many scenarios where you would want to store telemetry data for long-term analytics, apply advanced data transformations for an improved reporting performance, and to combine it with data from different environments, regions, and even across tenants. This is where Fabric warehouses shine, as they allow you to create custom tables with specific schemas and semantic models optimized for domain-specific analytics, such as the automation-centric examples in this guide. In this section of the guide we'll build a simple sample Fabric warehouse and transform and ingests data through a dataflow based on Dataflow Gen2. + +## Creating the warehouse + +> [!IMPORTANT] +> The warehouse scripts and tables provided below do not represent warehousing best practices. They are solely for demonstration purposes, illustrating what basic tables and field structures for automation telemetry might look like, and how to transform and ingest data using Fabric technology. + +1. Sign-in to Fabric (https://powerbi.com) +2. Choose the workspace you used in the previous sections of this guide. +3. Select the **+ New item** button in the command bar and choose Warehouse from the side-panel and give your warehouse a name i.e. `Automation CoE Warehouse` +4. Once in the warehouse experience, select **New SQL query** in the command bar and paste the following SQL CREATE TABLE statements and then select **Run**. + +### Create Date table for date-based intelligence + + ```sql + CREATE TABLE Dates + ( + [Date] [date] NOT NULL, + [DateAsText] [varchar](10) NOT NULL, + [Month] [int] NOT NULL, + [Day] [int] NOT NULL, + [Year] [smallint] NOT NULL, + [Quarter] [int] NOT NULL, + [DayName] [varchar](9) NOT NULL, + [DayInWeek] [int] NOT NULL, + [Week] [int] NOT NULL, + [MonthName] [varchar](9) NOT NULL, + [DaysInMonth] [int] NOT NULL, + [DayOfYear] [int] NOT NULL + ) + GO + + ``` + +### Create Flows table for desktop and cloud flows + + ```sql + CREATE TABLE Flows + ( + [statecode] [bigint] NULL, + [statuscode] [bigint] NULL, + [businessprocesstype] [bigint] NULL, + [category] [bigint] NULL, + [mode] [bigint] NULL, + [type] [bigint] NULL, + [uiflowtype] [bigint] NULL, + [createdby] [varchar](8000) NULL, + [modifiedby] [varchar](8000) NULL, + [owninguser] [varchar](8000) NULL, + [parentworkflowid] [varchar](8000) NULL, + [parentworkflowid_entitytype] [varchar](8000) NULL, + [ownerid] [varchar](8000) NULL, + [clientdata] [varchar](8000) NULL, + [connectionreferences] [varchar](8000) NULL, + [createdbyname] [varchar](8000) NULL, + [createdon] [datetime2](6) NULL, + [definition] [varchar](8000) NULL, + [dependencies] [varchar](8000) NULL, + [description] [varchar](8000) NULL, + [desktopflowmodules] [varchar](8000) NULL, + [inputs] [varchar](8000) NULL, + [modifiedbyname] [varchar](8000) NULL, + [modifiedon] [datetime2](6) NULL, + [name] [varchar](8000) NULL, + [outputs] [varchar](8000) NULL, + [resourceid] [varchar](8000) NULL, + [uniquename] [varchar](8000) NULL, + [workflowid] [varchar](8000) NULL, + [workflowidunique] [varchar](8000) NULL, + [TenantId] [varchar](8000) NULL, + [EnvironmentId] [varchar](8000) NULL, + [EnvironmentName] [varchar](8000) NULL, + [Region] [varchar](8000) NULL, + [isdesktopflow] [bit] NULL, + [Plaintext Password] [bigint] NULL, + [Hardcoded Email] [bigint] NULL, + [Hardcoded API Key] [bigint] NULL, + [Excessive Sleep] [bigint] NULL, + [Missing Error Handling] [bigint] NULL, + [Hardcoded File Path] [bigint] NULL, + [Web Endpoints and URL Shortcuts] [bigint] NULL, + [Advanced API Requests] [bigint] NULL, + [SQL Injection Risk] [bigint] NULL, + [SAP Fiori Access] [bigint] NULL, + [Has .Net] [bigint] NULL, + [Has Python] [bigint] NULL, + [Has JavaScript] [bigint] NULL, + [Has PowerShell] [bigint] NULL, + [Has VBScript] [bigint] NULL, + [Has DOS Command] [bigint] NULL, + [Has curl Command] [bigint] NULL, + [Governance Score] [float] NULL, + [Risk Score] [float] NULL, + [Risk Classification] [varchar](8000) NULL + ) + GO + ``` + +### Create CloudFlowRun table for cloud flow runs + + ```sql + CREATE TABLE CloudFlowRuns + ( + [createdby] [varchar](8000) NULL, + [workflow] [varchar](8000) NULL, + [ownerid] [varchar](8000) NULL, + [createdbyname] [varchar](8000) NULL, + [createdon] [datetime2](6) NULL, + [duration] [bigint] NULL, + [endtime] [datetime2](6) NULL, + [errorcode] [varchar](8000) NULL, + [errormessage] [varchar](8000) NULL, + [flowrunid] [varchar](8000) NULL, + [name] [varchar](8000) NULL, + [owneridname] [varchar](8000) NULL, + [parentrunid] [varchar](8000) NULL, + [PartitionId] [varchar](8000) NULL, + [resourceid] [varchar](8000) NULL, + [starttime] [datetime2](6) NULL, + [status] [varchar](8000) NULL, + [triggertype] [varchar](8000) NULL, + [ttlinseconds] [bigint] NULL, + [workflowid] [varchar](8000) NULL, + [workflowname] [varchar](8000) NULL, + [TenantId] [varchar](8000) NULL, + [EnvironmentId] [varchar](8000) NULL, + [EnvironmentName] [varchar](8000) NULL, + [Region] [varchar](8000) NULL, + [ReferenceDate] [date] NULL + ) + GO + + ``` + +### Create DesktopFlowRun table for desktop flow runs + + ```sql + CREATE TABLE DesktopFlowRuns + ( + [statecode] [bigint] NULL, + [statuscode] [bigint] NULL, + [runmode] [bigint] NULL, + [runsessionmode] [bigint] NULL, + [triggertype] [bigint] NULL, + [createdby] [varchar](8000) NULL, + [machinegroupid] [varchar](8000) NULL, + [machinegroupid_entitytype] [varchar](8000) NULL, + [machineid] [varchar](8000) NULL, + [machineid_entitytype] [varchar](8000) NULL, + [modifiedby] [varchar](8000) NULL, + [owningbusinessunit] [varchar](8000) NULL, + [owningteam] [varchar](8000) NULL, + [owninguser] [varchar](8000) NULL, + [regardingobjectid] [varchar](8000) NULL, + [regardingobjectid_entitytype] [varchar](8000) NULL, + [ownerid] [varchar](8000) NULL, + [additionalcontext] [varchar](8000) NULL, + [completedon] [datetime2](6) NULL, + [connectionid] [varchar](8000) NULL, + [context.parentWorkflowId] [varchar](8000) NULL, + [context.parentWorkflowRunId] [varchar](8000) NULL, + [context.attendedMode] [varchar](8000) NULL, + [context.connectionType] [varchar](8000) NULL, + [context.parentWorkflowType] [varchar](8000) NULL, + [correlationid] [varchar](8000) NULL, + [createdbyname] [varchar](8000) NULL, + [createdon] [datetime2](6) NULL, + [errorcode] [varchar](8000) NULL, + [errordetails] [varchar](8000) NULL, + [errorinnererror] [varchar](8000) NULL, + [errormessage] [varchar](8000) NULL, + [flowsessionid] [varchar](8000) NULL, + [gateway] [varchar](8000) NULL, + [inputs] [varchar](8000) NULL, + [machinegroupidname] [varchar](8000) NULL, + [machineidname] [varchar](8000) NULL, + [machinepercentcpuusage] [real] NULL, + [machinepercentramusage] [real] NULL, + [machineramusage] [bigint] NULL, + [modifiedbyname] [varchar](8000) NULL, + [modifiedon] [datetime2](6) NULL, + [name] [varchar](8000) NULL, + [outputs] [varchar](8000) NULL, + [owneridname] [varchar](8000) NULL, + [owneridtype] [varchar](8000) NULL, + [owningbusinessunitname] [varchar](8000) NULL, + [parentcloudflowrunsequenceid] [varchar](8000) NULL, + [parentworkflowid] [varchar](8000) NULL, + [processversion] [varchar](8000) NULL, + [regardingobjectidname] [varchar](8000) NULL, + [regardingobjecttypecode] [varchar](8000) NULL, + [rundetails] [varchar](8000) NULL, + [runduration] [bigint] NULL, + [runexecutionduration] [bigint] NULL, + [runwaitduration] [bigint] NULL, + [sessionusername] [varchar](8000) NULL, + [sessionusersid] [varchar](8000) NULL, + [startedon] [datetime2](6) NULL, + [IsDelete] [bit] NULL, + [PartitionId] [varchar](8000) NULL, + [TenantId] [varchar](8000) NULL, + [EnvironmentId] [varchar](8000) NULL, + [DesktopFlowRunDetailsUrl] [varchar](8000) NULL, + [EnvironmentName] [varchar](8000) NULL, + [Region] [varchar](8000) NULL, + [ReferenceDate] [date] NULL + ) + GO + ``` + +Your warehouse workspace should look similar to this now. +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-warehouse-table-creation-result.png" alt-text="Screenshot of a Fabric warehousing workspace with the list of tables that were created as part of this guide." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-warehouse-table-creation-result.png"::: + +## Manage default semantic model + +1. Ensure that you're in the Fabric warehousing workspace you used to generate the tables above and select the **Reporting** tab on top. +2. Select **Manage default semantic model** from the command bar, expand the tree node **dbo**, select all 4 tables and then **Confirm**. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png"::: + +## Create new semantic model + +Here's the outcome of the semantic model relationships we'll define next. +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png"::: + +1. Next, select **New semantic model** to create a new model we'll be using for reporting in Power BI later in this guide. +2. Give your semantic model a name i.e. `Automation Telemetry Model`, select all 4 tables again from the list and then **Confirm**. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/new-semantic-model.png"::: +3. Once the data modelling experiences opens, select the **Dates** table and click on the three dots '...' next to the table name and select **Mark as date table**. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/mark-as-date-table.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/mark-as-date-table.png"::: +4. Now, define the relationships by dragging and dropping related fields from their child tables to their parent based as outlined below. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/semantic-model-erd.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/semantic-model-erd.png"::: + + | Source table | Source column | Target table | Target column | Details | + |--------------------|-----------------------|--------------|------------------|--------------------------------------------| + | CloudFlowRuns | ReferenceDate | Dates | Date | CloudFlowRuns.ReferenceDate --> Dates.Date | + | CloudFlowRuns | workflowid | Flows | workflowid | CloudFlowRuns.workflowid --> Flows.workflowid | + | DesktopFlowRuns | ReferenceDate | Dates | Date | DesktopFlowRuns.ReferenceDate --> Dates.Date | + | DesktopFlowRuns | regardingobjectid | Flows | workflowid | 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z)xUNVARgl<=PjW7|BE_m@sX>Du7t$itV92P)1*(d|3V&shWvl{C!D%Z8j;SUCxn5% z%0E@Q7Q(9=%z|vq*utWw_s+c(gZ)o1^*Wq#)A3QT3yIvbK`*+dv%u+EfGxbSMAjAU za344bqS(qRG!NUB4^W&%8cr<%rT%6SMBAE>yfx^NG)Ez0vJ<5E#I~?n`gnVza-r_P z!wl;v17u!5vg97Y4AjPXz+W<)(x4hX17eIzU-9Qnt=7O6fC;s(5W|s>A>r4{cj4ay z7HcHsQ1$*7vc>1c3t!XIi)#e@FiP$7pQ$IbS#$qjgQSawm~aM`Iwt|)5ZFLK?8t~Q zF(8Oia#Le5K>Usos>GPje7j0Vj^byKNMYG{?`~+Q$v@o??YrbEqFw0RG{Sf8*@~ literal 0 HcmV?d00001 From 47befdb6b9d07f2fcb5e76cc67aa52ecdc12f200 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 09:30:26 +0100 Subject: [PATCH 23/44] Update automation-analytics-with-fabric-warehouse.md --- ...omation-analytics-with-fabric-warehouse.md | 374 +++++++++--------- 1 file changed, 187 insertions(+), 187 deletions(-) diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index 760488f8c..bac441715 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -32,201 +32,201 @@ There are many scenarios where you would want to store telemetry data for long-t ### Create Date table for date-based intelligence - ```sql - CREATE TABLE Dates - ( - [Date] [date] NOT NULL, - [DateAsText] [varchar](10) NOT NULL, - [Month] [int] NOT NULL, - [Day] [int] NOT NULL, - [Year] [smallint] NOT NULL, - [Quarter] [int] NOT NULL, - [DayName] [varchar](9) NOT NULL, - [DayInWeek] [int] NOT NULL, - [Week] [int] NOT NULL, - [MonthName] [varchar](9) NOT NULL, - [DaysInMonth] [int] NOT NULL, - [DayOfYear] [int] NOT NULL - ) - GO - - ``` +```sql + CREATE TABLE Dates + ( + [Date] [date] NOT NULL, + [DateAsText] [varchar](10) NOT NULL, + [Month] [int] NOT NULL, + [Day] [int] NOT NULL, + [Year] [smallint] NOT NULL, + [Quarter] [int] NOT NULL, + [DayName] [varchar](9) NOT NULL, + [DayInWeek] [int] NOT NULL, + [Week] [int] NOT NULL, + [MonthName] [varchar](9) NOT NULL, + [DaysInMonth] [int] NOT NULL, + [DayOfYear] [int] NOT NULL + ) + GO + +``` ### Create Flows table for desktop and cloud flows - ```sql - CREATE TABLE Flows - ( - [statecode] [bigint] NULL, - [statuscode] [bigint] NULL, - [businessprocesstype] [bigint] NULL, - [category] [bigint] NULL, - [mode] [bigint] NULL, - [type] [bigint] NULL, - [uiflowtype] [bigint] NULL, - [createdby] [varchar](8000) NULL, - [modifiedby] [varchar](8000) NULL, - [owninguser] [varchar](8000) NULL, - [parentworkflowid] [varchar](8000) NULL, - [parentworkflowid_entitytype] [varchar](8000) NULL, - [ownerid] [varchar](8000) NULL, - [clientdata] [varchar](8000) NULL, - [connectionreferences] [varchar](8000) NULL, - [createdbyname] [varchar](8000) NULL, - [createdon] [datetime2](6) NULL, - [definition] [varchar](8000) NULL, - [dependencies] [varchar](8000) NULL, - [description] [varchar](8000) NULL, - [desktopflowmodules] [varchar](8000) NULL, - [inputs] [varchar](8000) NULL, - [modifiedbyname] [varchar](8000) NULL, - [modifiedon] [datetime2](6) NULL, - [name] [varchar](8000) NULL, - [outputs] [varchar](8000) NULL, - [resourceid] [varchar](8000) NULL, - [uniquename] [varchar](8000) NULL, - [workflowid] [varchar](8000) NULL, - [workflowidunique] [varchar](8000) NULL, - [TenantId] [varchar](8000) NULL, - [EnvironmentId] [varchar](8000) NULL, - [EnvironmentName] [varchar](8000) NULL, - [Region] [varchar](8000) NULL, - [isdesktopflow] [bit] NULL, - [Plaintext Password] [bigint] NULL, - [Hardcoded Email] [bigint] NULL, - [Hardcoded API Key] [bigint] NULL, - [Excessive Sleep] [bigint] NULL, - [Missing Error Handling] [bigint] NULL, - [Hardcoded File Path] [bigint] NULL, - [Web Endpoints and URL Shortcuts] [bigint] NULL, - [Advanced API Requests] [bigint] NULL, - [SQL Injection Risk] [bigint] NULL, - [SAP Fiori Access] [bigint] NULL, - [Has .Net] [bigint] NULL, - [Has Python] [bigint] NULL, - [Has JavaScript] [bigint] NULL, - [Has PowerShell] [bigint] NULL, - [Has VBScript] [bigint] NULL, - [Has DOS Command] [bigint] NULL, - [Has curl Command] [bigint] NULL, - [Governance Score] [float] NULL, - [Risk Score] [float] NULL, - [Risk Classification] [varchar](8000) NULL - ) - GO - ``` - +```sql + CREATE TABLE Flows + ( + [statecode] [bigint] NULL, + [statuscode] [bigint] NULL, + [businessprocesstype] [bigint] NULL, + [category] [bigint] NULL, + [mode] [bigint] NULL, + [type] [bigint] NULL, + [uiflowtype] [bigint] NULL, + [createdby] [varchar](8000) NULL, + [modifiedby] [varchar](8000) NULL, + [owninguser] [varchar](8000) NULL, + [parentworkflowid] [varchar](8000) NULL, + [parentworkflowid_entitytype] [varchar](8000) NULL, + [ownerid] [varchar](8000) NULL, + [clientdata] [varchar](8000) NULL, + [connectionreferences] [varchar](8000) NULL, + [createdbyname] [varchar](8000) NULL, + [createdon] [datetime2](6) NULL, + [definition] [varchar](8000) NULL, + [dependencies] [varchar](8000) NULL, + [description] [varchar](8000) NULL, + [desktopflowmodules] [varchar](8000) NULL, + [inputs] [varchar](8000) NULL, + [modifiedbyname] [varchar](8000) NULL, + [modifiedon] [datetime2](6) NULL, + [name] [varchar](8000) NULL, + [outputs] [varchar](8000) NULL, + [resourceid] [varchar](8000) NULL, + [uniquename] [varchar](8000) NULL, + [workflowid] [varchar](8000) NULL, + [workflowidunique] [varchar](8000) NULL, + [TenantId] [varchar](8000) NULL, + [EnvironmentId] [varchar](8000) NULL, + [EnvironmentName] [varchar](8000) NULL, + [Region] [varchar](8000) NULL, + [isdesktopflow] [bit] NULL, + [Plaintext Password] [bigint] NULL, + [Hardcoded Email] [bigint] NULL, + [Hardcoded API Key] [bigint] NULL, + [Excessive Sleep] [bigint] NULL, + [Missing Error Handling] [bigint] NULL, + [Hardcoded File Path] [bigint] NULL, + [Web Endpoints and URL Shortcuts] [bigint] NULL, + [Advanced API Requests] [bigint] NULL, + [SQL Injection Risk] [bigint] NULL, + [SAP Fiori Access] [bigint] NULL, + [Has .Net] [bigint] NULL, + [Has Python] [bigint] NULL, + [Has JavaScript] [bigint] NULL, + [Has PowerShell] [bigint] NULL, + [Has VBScript] [bigint] NULL, + [Has DOS Command] [bigint] NULL, + [Has curl Command] [bigint] NULL, + [Governance Score] [float] NULL, + [Risk Score] [float] NULL, + [Risk Classification] [varchar](8000) NULL + ) + GO +``` + ### Create CloudFlowRun table for cloud flow runs - ```sql - CREATE TABLE CloudFlowRuns - ( - [createdby] [varchar](8000) NULL, - [workflow] [varchar](8000) NULL, - [ownerid] [varchar](8000) NULL, - [createdbyname] [varchar](8000) NULL, - [createdon] [datetime2](6) NULL, - [duration] [bigint] NULL, - [endtime] [datetime2](6) NULL, - [errorcode] [varchar](8000) NULL, - [errormessage] [varchar](8000) NULL, - [flowrunid] [varchar](8000) NULL, - [name] [varchar](8000) NULL, - [owneridname] [varchar](8000) NULL, - [parentrunid] [varchar](8000) NULL, - [PartitionId] [varchar](8000) NULL, - [resourceid] [varchar](8000) NULL, - [starttime] [datetime2](6) NULL, - [status] [varchar](8000) NULL, - [triggertype] [varchar](8000) NULL, - [ttlinseconds] [bigint] NULL, - [workflowid] [varchar](8000) NULL, - [workflowname] [varchar](8000) NULL, - [TenantId] [varchar](8000) NULL, - [EnvironmentId] [varchar](8000) NULL, - [EnvironmentName] [varchar](8000) NULL, - [Region] [varchar](8000) NULL, - [ReferenceDate] [date] NULL - ) - GO +```sql + CREATE TABLE CloudFlowRuns + ( + [createdby] [varchar](8000) NULL, + [workflow] [varchar](8000) NULL, + [ownerid] [varchar](8000) NULL, + [createdbyname] [varchar](8000) NULL, + [createdon] [datetime2](6) NULL, + [duration] [bigint] NULL, + [endtime] [datetime2](6) NULL, + [errorcode] [varchar](8000) NULL, + [errormessage] [varchar](8000) NULL, + [flowrunid] [varchar](8000) NULL, + [name] [varchar](8000) NULL, + [owneridname] [varchar](8000) NULL, + [parentrunid] [varchar](8000) NULL, + [PartitionId] [varchar](8000) NULL, + [resourceid] [varchar](8000) NULL, + [starttime] [datetime2](6) NULL, + [status] [varchar](8000) NULL, + [triggertype] [varchar](8000) NULL, + [ttlinseconds] [bigint] NULL, + [workflowid] [varchar](8000) NULL, + [workflowname] [varchar](8000) NULL, + [TenantId] [varchar](8000) NULL, + [EnvironmentId] [varchar](8000) NULL, + [EnvironmentName] [varchar](8000) NULL, + [Region] [varchar](8000) NULL, + [ReferenceDate] [date] NULL + ) + GO + +``` - ``` - ### Create DesktopFlowRun table for desktop flow runs - ```sql - CREATE TABLE DesktopFlowRuns - ( - [statecode] [bigint] NULL, - [statuscode] [bigint] NULL, - [runmode] [bigint] NULL, - [runsessionmode] [bigint] NULL, - [triggertype] [bigint] NULL, - [createdby] [varchar](8000) NULL, - [machinegroupid] [varchar](8000) NULL, - [machinegroupid_entitytype] [varchar](8000) NULL, - [machineid] [varchar](8000) NULL, - [machineid_entitytype] [varchar](8000) NULL, - [modifiedby] [varchar](8000) NULL, - [owningbusinessunit] [varchar](8000) NULL, - [owningteam] [varchar](8000) NULL, - [owninguser] [varchar](8000) NULL, - [regardingobjectid] [varchar](8000) NULL, - [regardingobjectid_entitytype] [varchar](8000) NULL, - [ownerid] [varchar](8000) NULL, - [additionalcontext] [varchar](8000) NULL, - [completedon] [datetime2](6) NULL, - [connectionid] [varchar](8000) NULL, - [context.parentWorkflowId] [varchar](8000) NULL, - [context.parentWorkflowRunId] [varchar](8000) NULL, - [context.attendedMode] [varchar](8000) NULL, - [context.connectionType] [varchar](8000) NULL, - [context.parentWorkflowType] [varchar](8000) NULL, - [correlationid] [varchar](8000) NULL, - [createdbyname] [varchar](8000) NULL, - [createdon] [datetime2](6) NULL, - [errorcode] [varchar](8000) NULL, - [errordetails] [varchar](8000) NULL, - [errorinnererror] [varchar](8000) NULL, - [errormessage] [varchar](8000) NULL, - [flowsessionid] [varchar](8000) NULL, - [gateway] [varchar](8000) NULL, - [inputs] [varchar](8000) NULL, - [machinegroupidname] [varchar](8000) NULL, - [machineidname] [varchar](8000) NULL, - [machinepercentcpuusage] [real] NULL, - [machinepercentramusage] [real] NULL, - [machineramusage] [bigint] NULL, - [modifiedbyname] [varchar](8000) NULL, - [modifiedon] [datetime2](6) NULL, - [name] [varchar](8000) NULL, - [outputs] [varchar](8000) NULL, - [owneridname] [varchar](8000) NULL, - [owneridtype] [varchar](8000) NULL, - [owningbusinessunitname] [varchar](8000) NULL, - [parentcloudflowrunsequenceid] [varchar](8000) NULL, - [parentworkflowid] [varchar](8000) NULL, - [processversion] [varchar](8000) NULL, - [regardingobjectidname] [varchar](8000) NULL, - [regardingobjecttypecode] [varchar](8000) NULL, - [rundetails] [varchar](8000) NULL, - [runduration] [bigint] NULL, - [runexecutionduration] [bigint] NULL, - [runwaitduration] [bigint] NULL, - [sessionusername] [varchar](8000) NULL, - [sessionusersid] [varchar](8000) NULL, - [startedon] [datetime2](6) NULL, - [IsDelete] [bit] NULL, - [PartitionId] [varchar](8000) NULL, - [TenantId] [varchar](8000) NULL, - [EnvironmentId] [varchar](8000) NULL, - [DesktopFlowRunDetailsUrl] [varchar](8000) NULL, - [EnvironmentName] [varchar](8000) NULL, - [Region] [varchar](8000) NULL, - [ReferenceDate] [date] NULL - ) - GO - ``` +```sql + CREATE TABLE DesktopFlowRuns + ( + [statecode] [bigint] NULL, + [statuscode] [bigint] NULL, + [runmode] [bigint] NULL, + [runsessionmode] [bigint] NULL, + [triggertype] [bigint] NULL, + [createdby] [varchar](8000) NULL, + [machinegroupid] [varchar](8000) NULL, + [machinegroupid_entitytype] [varchar](8000) NULL, + [machineid] [varchar](8000) NULL, + [machineid_entitytype] [varchar](8000) NULL, + [modifiedby] [varchar](8000) NULL, + [owningbusinessunit] [varchar](8000) NULL, + [owningteam] [varchar](8000) NULL, + [owninguser] [varchar](8000) NULL, + [regardingobjectid] [varchar](8000) NULL, + [regardingobjectid_entitytype] [varchar](8000) NULL, + [ownerid] [varchar](8000) NULL, + [additionalcontext] [varchar](8000) NULL, + [completedon] [datetime2](6) NULL, + [connectionid] [varchar](8000) NULL, + [context.parentWorkflowId] [varchar](8000) NULL, + [context.parentWorkflowRunId] [varchar](8000) NULL, + [context.attendedMode] [varchar](8000) NULL, + [context.connectionType] [varchar](8000) NULL, + [context.parentWorkflowType] [varchar](8000) NULL, + [correlationid] [varchar](8000) NULL, + [createdbyname] [varchar](8000) NULL, + [createdon] [datetime2](6) NULL, + [errorcode] [varchar](8000) NULL, + [errordetails] [varchar](8000) NULL, + [errorinnererror] [varchar](8000) NULL, + [errormessage] [varchar](8000) NULL, + [flowsessionid] [varchar](8000) NULL, + [gateway] [varchar](8000) NULL, + [inputs] [varchar](8000) NULL, + [machinegroupidname] [varchar](8000) NULL, + [machineidname] [varchar](8000) NULL, + [machinepercentcpuusage] [real] NULL, + [machinepercentramusage] [real] NULL, + [machineramusage] [bigint] NULL, + [modifiedbyname] [varchar](8000) NULL, + [modifiedon] [datetime2](6) NULL, + [name] [varchar](8000) NULL, + [outputs] [varchar](8000) NULL, + [owneridname] [varchar](8000) NULL, + [owneridtype] [varchar](8000) NULL, + [owningbusinessunitname] [varchar](8000) NULL, + [parentcloudflowrunsequenceid] [varchar](8000) NULL, + [parentworkflowid] [varchar](8000) NULL, + [processversion] [varchar](8000) NULL, + [regardingobjectidname] [varchar](8000) NULL, + [regardingobjecttypecode] [varchar](8000) NULL, + [rundetails] [varchar](8000) NULL, + [runduration] [bigint] NULL, + [runexecutionduration] [bigint] NULL, + [runwaitduration] [bigint] NULL, + [sessionusername] [varchar](8000) NULL, + [sessionusersid] [varchar](8000) NULL, + [startedon] [datetime2](6) NULL, + [IsDelete] [bit] NULL, + [PartitionId] [varchar](8000) NULL, + [TenantId] [varchar](8000) NULL, + [EnvironmentId] [varchar](8000) NULL, + [DesktopFlowRunDetailsUrl] [varchar](8000) NULL, + [EnvironmentName] [varchar](8000) NULL, + [Region] [varchar](8000) NULL, + [ReferenceDate] [date] NULL + ) + GO +``` Your warehouse workspace should look similar to this now. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-warehouse-table-creation-result.png" alt-text="Screenshot of a Fabric warehousing workspace with the list of tables that were created as part of this guide." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-warehouse-table-creation-result.png"::: From 44e153d22c3b4f57c6fa763f9102e93d08f26653 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 09:31:55 +0100 Subject: [PATCH 24/44] Update automation-analytics-with-fabric-warehouse.md --- articles/automation-analytics-with-fabric-warehouse.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index bac441715..f8aef9267 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -239,9 +239,6 @@ Your warehouse workspace should look similar to this now. ## Create new semantic model -Here's the outcome of the semantic model relationships we'll define next. -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png"::: - 1. Next, select **New semantic model** to create a new model we'll be using for reporting in Power BI later in this guide. 2. Give your semantic model a name i.e. `Automation Telemetry Model`, select all 4 tables again from the list and then **Confirm**. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/new-semantic-model.png"::: @@ -257,6 +254,9 @@ Here's the outcome of the semantic model relationships we'll define next. | DesktopFlowRuns | ReferenceDate | Dates | Date | DesktopFlowRuns.ReferenceDate --> Dates.Date | | DesktopFlowRuns | regardingobjectid | Flows | workflowid | DesktopFlowRuns.regardingobjectid --> Flows.workflowid | +Here's the outcome of the semantic model relationships we just defined. +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png"::: + ## What's next > [!div class="nextstepaction"] From 723da7973bbd298295d732a25b6a0ddbf6e8053f Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 09:44:01 +0100 Subject: [PATCH 25/44] Structure changes --- articles/TOC.yml | 4 ++-- ...utomation-analytics-with-fabric-queries.md | 2 +- ...utomation-analytics-with-fabric-reports.md | 2 +- ...omation-analytics-with-fabric-warehouse.md | 6 +++--- .../mark-as-date-table.png | Bin 274144 -> 151746 bytes .../semantic-model-erd.png | Bin 43092 -> 44831 bytes 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/articles/TOC.yml b/articles/TOC.yml index 65f62ee1d..d063cd95d 100644 --- a/articles/TOC.yml +++ b/articles/TOC.yml @@ -1127,10 +1127,10 @@ href: automation-analytics-with-fabric-introduction.md - name: Crafting SQL analytics queries with Fabric href: automation-analytics-with-fabric-queries.md - - name: Building custom Power BI reports in Fabric - href: automation-analytics-with-fabric-reports.md - name: Designing an automation-centric Fabric warehouse href: automation-analytics-with-fabric-warehouse.md + - name: Building custom Power BI reports in Fabric + href: automation-analytics-with-fabric-reports.md - name: Setting up real-time alerts with Reflex href: automation-analytics-with-fabric-alerts.md - name: Capacity utilization diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index a08e8397c..664bfec0a 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -537,7 +537,7 @@ This query returns the top 10 failing actions by number of errors over the past ## What's next > [!div class="nextstepaction"] -> [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) +> [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) ## Learn more diff --git a/articles/automation-analytics-with-fabric-reports.md b/articles/automation-analytics-with-fabric-reports.md index 37f7cccea..11bb1dc9a 100644 --- a/articles/automation-analytics-with-fabric-reports.md +++ b/articles/automation-analytics-with-fabric-reports.md @@ -21,7 +21,7 @@ search.audienceType: ## What's next > [!div class="nextstepaction"] -> [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) +> [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) ## Learn more diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index f8aef9267..5401ef9a1 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -254,13 +254,13 @@ Your warehouse workspace should look similar to this now. | DesktopFlowRuns | ReferenceDate | Dates | Date | DesktopFlowRuns.ReferenceDate --> Dates.Date | | DesktopFlowRuns | regardingobjectid | Flows | workflowid | DesktopFlowRuns.regardingobjectid --> Flows.workflowid | -Here's the outcome of the semantic model relationships we just defined. -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png"::: + Here's the outcome of the semantic model relationships we just defined. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png"::: ## What's next > [!div class="nextstepaction"] -> [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) +> [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) ## Learn more diff --git a/articles/media/advanced-automation-related-data-analytics-fabric/mark-as-date-table.png b/articles/media/advanced-automation-related-data-analytics-fabric/mark-as-date-table.png index ba505e993cc20d5af7ca95107cf7d3a42c7274e2..54f3d0fcbda5149fc60fca853e795f0b3211013d 100644 GIT binary patch delta 10116 zcmbuFby!r}+xLeM5R?)DrBfQDB^3sd6eOjU?vNf}lTuPDpfo5**N{VlfP#QX3>|Wi zE{UOe7Uvh|{NDF{uIs&?XZ^!m%G--SG)p$xGoH_zL7!3BTmx3mN zi5ZWXg3b0JDo80z39DKO+kq3R^}?@EOIx{UZF!Jil-Xyg=jk?P!^5IO$Q+)?ks8s7 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z)xUNVARgl<=PjW7|BE_m@sX>Du7t$itV92P)1*(d|3V&shWvl{C!D%Z8j;SUCxn5% z%0E@Q7Q(9=%z|vq*utWw_s+c(gZ)o1^*Wq#)A3QT3yIvbK`*+dv%u+EfGxbSMAjAU za344bqS(qRG!NUB4^W&%8cr<%rT%6SMBAE>yfx^NG)Ez0vJ<5E#I~?n`gnVza-r_P z!wl;v17u!5vg97Y4AjPXz+W<)(x4hX17eIzU-9Qnt=7O6fC;s(5W|s>A>r4{cj4ay z7HcHsQ1$*7vc>1c3t!XIi)#e@FiP$7pQ$IbS#$qjgQSawm~aM`Iwt|)5ZFLK?8t~Q zF(8Oia#Le5K>Usos>GPje7j0Vj^byKNMYG{?`~+Q$v@o??YrbEqFw0RG{Sf8*@~ From 09695e9a3d1041e05fa74d746a9300b36fab0e91 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 09:56:00 +0100 Subject: [PATCH 26/44] Structure changes --- articles/TOC.yml | 6 +++--- articles/automation-analytics-with-fabric-reports.md | 4 ++-- articles/automation-analytics-with-fabric-warehouse.md | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/articles/TOC.yml b/articles/TOC.yml index d063cd95d..c962135e3 100644 --- a/articles/TOC.yml +++ b/articles/TOC.yml @@ -1125,11 +1125,11 @@ items: - name: Overview href: automation-analytics-with-fabric-introduction.md - - name: Crafting SQL analytics queries with Fabric + - name: Crafting automation-centric ad-hoc queries href: automation-analytics-with-fabric-queries.md - - name: Designing an automation-centric Fabric warehouse + - name: Designing automation-centric warehouse href: automation-analytics-with-fabric-warehouse.md - - name: Building custom Power BI reports in Fabric + - name: Building automation-centric reports href: automation-analytics-with-fabric-reports.md - name: Setting up real-time alerts with Reflex href: automation-analytics-with-fabric-alerts.md diff --git a/articles/automation-analytics-with-fabric-reports.md b/articles/automation-analytics-with-fabric-reports.md index 11bb1dc9a..3d066a2a7 100644 --- a/articles/automation-analytics-with-fabric-reports.md +++ b/articles/automation-analytics-with-fabric-reports.md @@ -1,5 +1,5 @@ --- -title: Building custom Power BI reports for data insights in Fabric +title: Building custom reports for data insights in Fabric description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. ms.topic: conceptual ms.date: 11/06/2024 @@ -16,7 +16,7 @@ search.audienceType: - enduser --- -# Building custom Power BI reports for data insights in Fabric +# Building automation-centric reports in Fabric ## What's next diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index 5401ef9a1..f14042e52 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -247,7 +247,7 @@ Your warehouse workspace should look similar to this now. 4. Now, define the relationships by dragging and dropping related fields from their child tables to their parent based as outlined below. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/semantic-model-erd.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/semantic-model-erd.png"::: - | Source table | Source column | Target table | Target column | Details | + | Source table | Source column | Target table | Target column | Details | |--------------------|-----------------------|--------------|------------------|--------------------------------------------| | CloudFlowRuns | ReferenceDate | Dates | Date | CloudFlowRuns.ReferenceDate --> Dates.Date | | CloudFlowRuns | workflowid | Flows | workflowid | CloudFlowRuns.workflowid --> Flows.workflowid | From e865f78b7be391b5f69b19b5e5a412b7092e3aba Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 11:52:04 +0100 Subject: [PATCH 27/44] Smaller updates --- articles/automation-analytics-with-fabric-alerts.md | 3 +-- articles/automation-analytics-with-fabric-introduction.md | 3 +-- articles/automation-analytics-with-fabric-queries.md | 3 +-- articles/automation-analytics-with-fabric-reports.md | 3 +-- articles/automation-analytics-with-fabric-warehouse.md | 3 +-- 5 files changed, 5 insertions(+), 10 deletions(-) diff --git a/articles/automation-analytics-with-fabric-alerts.md b/articles/automation-analytics-with-fabric-alerts.md index 3d18c66a6..b906f9647 100644 --- a/articles/automation-analytics-with-fabric-alerts.md +++ b/articles/automation-analytics-with-fabric-alerts.md @@ -4,9 +4,8 @@ description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to b ms.topic: conceptual ms.date: 11/06/2024 ms.author: appapaio -ms.reviewer: dmartens +ms.reviewer: contributors: - - DanaMartens author: rpapostolis ms.collection: conceptual search.audienceType: diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 631b0bc10..4378591a1 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -4,9 +4,8 @@ description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to b ms.topic: conceptual ms.date: 11/06/2024 ms.author: appapaio -ms.reviewer: dmartens +ms.reviewer: contributors: - - DanaMartens author: rpapostolis ms.collection: conceptual search.audienceType: diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 664bfec0a..baec14906 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -4,9 +4,8 @@ description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to b ms.topic: conceptual ms.date: 11/06/2024 ms.author: appapaio -ms.reviewer: dmartens +ms.reviewer: contributors: - - DanaMartens author: rpapostolis ms.collection: conceptual search.audienceType: diff --git a/articles/automation-analytics-with-fabric-reports.md b/articles/automation-analytics-with-fabric-reports.md index 3d066a2a7..8f078d2ab 100644 --- a/articles/automation-analytics-with-fabric-reports.md +++ b/articles/automation-analytics-with-fabric-reports.md @@ -4,9 +4,8 @@ description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to b ms.topic: conceptual ms.date: 11/06/2024 ms.author: appapaio -ms.reviewer: dmartens +ms.reviewer: contributors: - - DanaMartens author: rpapostolis ms.collection: conceptual search.audienceType: diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index f14042e52..38e8675fc 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -4,9 +4,8 @@ description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to b ms.topic: conceptual ms.date: 11/06/2024 ms.author: appapaio -ms.reviewer: dmartens +ms.reviewer: contributors: - - DanaMartens author: rpapostolis ms.collection: conceptual search.audienceType: From a345643a05877c19e3c2d38a4b0044eae52e9685 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 15:03:10 +0100 Subject: [PATCH 28/44] Update automation-analytics-with-fabric-warehouse.md --- articles/automation-analytics-with-fabric-warehouse.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index 38e8675fc..23b86cf11 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -100,7 +100,6 @@ There are many scenarios where you would want to store telemetry data for long-t [Web Endpoints and URL Shortcuts] [bigint] NULL, [Advanced API Requests] [bigint] NULL, [SQL Injection Risk] [bigint] NULL, - [SAP Fiori Access] [bigint] NULL, [Has .Net] [bigint] NULL, [Has Python] [bigint] NULL, [Has JavaScript] [bigint] NULL, @@ -219,7 +218,6 @@ There are many scenarios where you would want to store telemetry data for long-t [PartitionId] [varchar](8000) NULL, [TenantId] [varchar](8000) NULL, [EnvironmentId] [varchar](8000) NULL, - [DesktopFlowRunDetailsUrl] [varchar](8000) NULL, [EnvironmentName] [varchar](8000) NULL, [Region] [varchar](8000) NULL, [ReferenceDate] [date] NULL From d9879fde9c497c441b3f012e784fd4aca1f4d31b Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 15:06:35 +0100 Subject: [PATCH 29/44] Update automation-analytics-with-fabric-warehouse.md --- articles/automation-analytics-with-fabric-warehouse.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md index 23b86cf11..027fc3097 100644 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ b/articles/automation-analytics-with-fabric-warehouse.md @@ -106,10 +106,7 @@ There are many scenarios where you would want to store telemetry data for long-t [Has PowerShell] [bigint] NULL, [Has VBScript] [bigint] NULL, [Has DOS Command] [bigint] NULL, - [Has curl Command] [bigint] NULL, - [Governance Score] [float] NULL, - [Risk Score] [float] NULL, - [Risk Classification] [varchar](8000) NULL + [Has curl Command] [bigint] NULL ) GO ``` From 71b77e7fb3524e4ec43d5da766a679ad021d0106 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Mon, 11 Nov 2024 16:56:14 +0100 Subject: [PATCH 30/44] Update automation-analytics-with-fabric-queries.md --- ...utomation-analytics-with-fabric-queries.md | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index baec14906..1c0539e93 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -22,6 +22,28 @@ search.audienceType: > > **Disclaimer:** The scenarios, query examples, and data used in this tutorial are fictional, may include errors, inefficiencies and are intended solely for demonstration purposes. +## List of automation-related tables + +The following table includes automation-related tables that are frequently used for reporting and observability use cases. + +| Display name | Object name | Purpose | +|--------------------|------------------|-------------------------------------------------------| +| [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | +| [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | +| [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | +| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | +| [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | +| [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | +| [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | +| [Work Queue Item](/power-apps/developer/data-platform/reference/entities/workqueueitem) | workqueueitem | Contains information about each run of a workflow.| +| [User](/power-apps/developer/data-platform/reference/entities/systemuser) | systemuser | Represents an instance of a workflow execution. | + +### Simplified table relationship diagram + +The image includes only relations to tables that are included above and automation-relevant. + +:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png"::: + Step-by-step instructions to create a sample SQL Query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. 1. Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com) and log in with your credentials. From 94a0d012e87e3fac9608d5fd875637f31e9d9de3 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 14 Nov 2024 15:12:49 +0100 Subject: [PATCH 31/44] Update automation-analytics-with-fabric-queries.md --- articles/automation-analytics-with-fabric-queries.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 1c0539e93..2d99afc97 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -31,7 +31,7 @@ The following table includes automation-related tables that are frequently used | [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | | [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | | [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | -| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowrun | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | +| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowlog | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | | [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | @@ -44,6 +44,8 @@ The image includes only relations to tables that are included above and automati :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png"::: +### Create your first query in Fabric + Step-by-step instructions to create a sample SQL Query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. 1. Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com) and log in with your credentials. From 7cf1e37dc1af524a4810fd812be7d2e5620cae85 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Tue, 19 Nov 2024 12:57:41 +0100 Subject: [PATCH 32/44] Update automation-analytics-with-fabric-queries.md --- articles/automation-analytics-with-fabric-queries.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 2d99afc97..8a5d4daf6 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -67,7 +67,7 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End runwaitduration, context FROM - flowsessions + flowsession WHERE regardingobjectid = '[specific_flow_id]' -- Replace with the actual flow ID AND machineid = '[specific_machine_guid]' -- Replace with the actual machine ID From 058c7e698bb07bb56710a3089ad2a033b562c900 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 19 Dec 2024 10:03:49 +0100 Subject: [PATCH 33/44] Removed future extensions --- articles/TOC.yml | 6 - ...automation-analytics-with-fabric-alerts.md | 25 -- ...tion-analytics-with-fabric-introduction.md | 7 - ...utomation-analytics-with-fabric-queries.md | 15 +- ...utomation-analytics-with-fabric-reports.md | 30 -- ...omation-analytics-with-fabric-warehouse.md | 264 ------------------ 6 files changed, 1 insertion(+), 346 deletions(-) delete mode 100644 articles/automation-analytics-with-fabric-alerts.md delete mode 100644 articles/automation-analytics-with-fabric-reports.md delete mode 100644 articles/automation-analytics-with-fabric-warehouse.md diff --git a/articles/TOC.yml b/articles/TOC.yml index c962135e3..738b1bed2 100644 --- a/articles/TOC.yml +++ b/articles/TOC.yml @@ -1127,12 +1127,6 @@ href: automation-analytics-with-fabric-introduction.md - name: Crafting automation-centric ad-hoc queries href: automation-analytics-with-fabric-queries.md - - name: Designing automation-centric warehouse - href: automation-analytics-with-fabric-warehouse.md - - name: Building automation-centric reports - href: automation-analytics-with-fabric-reports.md - - name: Setting up real-time alerts with Reflex - href: automation-analytics-with-fabric-alerts.md - name: Capacity utilization items: - name: Introduction to capacity utilization (preview) diff --git a/articles/automation-analytics-with-fabric-alerts.md b/articles/automation-analytics-with-fabric-alerts.md deleted file mode 100644 index b906f9647..000000000 --- a/articles/automation-analytics-with-fabric-alerts.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -title: Setting up real-time alerts with Reflex for proactive monitoring -description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. -ms.topic: conceptual -ms.date: 11/06/2024 -ms.author: appapaio -ms.reviewer: -contributors: -author: rpapostolis -ms.collection: conceptual -search.audienceType: - - admin - - coe - - flowmaker - - enduser ---- - -# Setting up real-time alerts with Reflex for proactive monitoring - -## Learn more - -- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) -- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) -- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) -- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 4378591a1..7c8a95b6b 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -58,10 +58,3 @@ Before you continue, ensure you meet the following prerequisites: > [!div class="nextstepaction"] > [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) - -## Learn more - -- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) -- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) -- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) -- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 8a5d4daf6..23ceb0dcd 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -1,6 +1,6 @@ --- title: Crafting automation-related queries with Fabric -description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. +description: Learn how to query automation-related Dataverse data in Microsoft Fabric ms.topic: conceptual ms.date: 11/06/2024 ms.author: appapaio @@ -17,7 +17,6 @@ search.audienceType: ## Crafting automation-related queries with Fabric - > [!NOTE] > > **Disclaimer:** The scenarios, query examples, and data used in this tutorial are fictional, may include errors, inefficiencies and are intended solely for demonstration purposes. @@ -556,15 +555,3 @@ This query returns the top 10 failing actions by number of errors over the past ORDER BY OccurrenceCount DESC; ``` - -## What's next - -> [!div class="nextstepaction"] -> [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) - -## Learn more - -- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) -- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) -- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) -- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) \ No newline at end of file diff --git a/articles/automation-analytics-with-fabric-reports.md b/articles/automation-analytics-with-fabric-reports.md deleted file mode 100644 index 8f078d2ab..000000000 --- a/articles/automation-analytics-with-fabric-reports.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -title: Building custom reports for data insights in Fabric -description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. -ms.topic: conceptual -ms.date: 11/06/2024 -ms.author: appapaio -ms.reviewer: -contributors: -author: rpapostolis -ms.collection: conceptual -search.audienceType: - - admin - - coe - - flowmaker - - enduser ---- - -# Building automation-centric reports in Fabric - -## What's next - -> [!div class="nextstepaction"] -> [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) - -## Learn more - -- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) -- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) -- [Designing an automation-centric Fabric warehouse](automation-analytics-with-fabric-warehouse.md) -- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) \ No newline at end of file diff --git a/articles/automation-analytics-with-fabric-warehouse.md b/articles/automation-analytics-with-fabric-warehouse.md deleted file mode 100644 index 027fc3097..000000000 --- a/articles/automation-analytics-with-fabric-warehouse.md +++ /dev/null @@ -1,264 +0,0 @@ ---- -title: Designing an automation-centric Fabric warehouse -description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. -ms.topic: conceptual -ms.date: 11/06/2024 -ms.author: appapaio -ms.reviewer: -contributors: -author: rpapostolis -ms.collection: conceptual -search.audienceType: - - admin - - coe - - flowmaker - - enduser ---- - -# Designing an automation-centric Fabric warehouse - -There are many scenarios where you would want to store telemetry data for long-term analytics, apply advanced data transformations for an improved reporting performance, and to combine it with data from different environments, regions, and even across tenants. This is where Fabric warehouses shine, as they allow you to create custom tables with specific schemas and semantic models optimized for domain-specific analytics, such as the automation-centric examples in this guide. In this section of the guide we'll build a simple sample Fabric warehouse and transform and ingests data through a dataflow based on Dataflow Gen2. - -## Creating the warehouse - -> [!IMPORTANT] -> The warehouse scripts and tables provided below do not represent warehousing best practices. They are solely for demonstration purposes, illustrating what basic tables and field structures for automation telemetry might look like, and how to transform and ingest data using Fabric technology. - -1. Sign-in to Fabric (https://powerbi.com) -2. Choose the workspace you used in the previous sections of this guide. -3. Select the **+ New item** button in the command bar and choose Warehouse from the side-panel and give your warehouse a name i.e. `Automation CoE Warehouse` -4. Once in the warehouse experience, select **New SQL query** in the command bar and paste the following SQL CREATE TABLE statements and then select **Run**. - -### Create Date table for date-based intelligence - -```sql - CREATE TABLE Dates - ( - [Date] [date] NOT NULL, - [DateAsText] [varchar](10) NOT NULL, - [Month] [int] NOT NULL, - [Day] [int] NOT NULL, - [Year] [smallint] NOT NULL, - [Quarter] [int] NOT NULL, - [DayName] [varchar](9) NOT NULL, - [DayInWeek] [int] NOT NULL, - [Week] [int] NOT NULL, - [MonthName] [varchar](9) NOT NULL, - [DaysInMonth] [int] NOT NULL, - [DayOfYear] [int] NOT NULL - ) - GO - -``` - -### Create Flows table for desktop and cloud flows - -```sql - CREATE TABLE Flows - ( - [statecode] [bigint] NULL, - [statuscode] [bigint] NULL, - [businessprocesstype] [bigint] NULL, - [category] [bigint] NULL, - [mode] [bigint] NULL, - [type] [bigint] NULL, - [uiflowtype] [bigint] NULL, - [createdby] [varchar](8000) NULL, - [modifiedby] [varchar](8000) NULL, - [owninguser] [varchar](8000) NULL, - [parentworkflowid] [varchar](8000) NULL, - [parentworkflowid_entitytype] [varchar](8000) NULL, - [ownerid] [varchar](8000) NULL, - [clientdata] [varchar](8000) NULL, - [connectionreferences] [varchar](8000) NULL, - [createdbyname] [varchar](8000) NULL, - [createdon] [datetime2](6) NULL, - [definition] [varchar](8000) NULL, - [dependencies] [varchar](8000) NULL, - [description] [varchar](8000) NULL, - [desktopflowmodules] [varchar](8000) NULL, - [inputs] [varchar](8000) NULL, - [modifiedbyname] [varchar](8000) NULL, - [modifiedon] [datetime2](6) NULL, - [name] [varchar](8000) NULL, - [outputs] [varchar](8000) NULL, - [resourceid] [varchar](8000) NULL, - [uniquename] [varchar](8000) NULL, - [workflowid] [varchar](8000) NULL, - [workflowidunique] [varchar](8000) NULL, - [TenantId] [varchar](8000) NULL, - [EnvironmentId] [varchar](8000) NULL, - [EnvironmentName] [varchar](8000) NULL, - [Region] [varchar](8000) NULL, - [isdesktopflow] [bit] NULL, - [Plaintext Password] [bigint] NULL, - [Hardcoded Email] [bigint] NULL, - [Hardcoded API Key] [bigint] NULL, - [Excessive Sleep] [bigint] NULL, - [Missing Error Handling] [bigint] NULL, - [Hardcoded File Path] [bigint] NULL, - [Web Endpoints and URL Shortcuts] [bigint] NULL, - [Advanced API Requests] [bigint] NULL, - [SQL Injection Risk] [bigint] NULL, - [Has .Net] [bigint] NULL, - [Has Python] [bigint] NULL, - [Has JavaScript] [bigint] NULL, - [Has PowerShell] [bigint] NULL, - [Has VBScript] [bigint] NULL, - [Has DOS Command] [bigint] NULL, - [Has curl Command] [bigint] NULL - ) - GO -``` - -### Create CloudFlowRun table for cloud flow runs - -```sql - CREATE TABLE CloudFlowRuns - ( - [createdby] [varchar](8000) NULL, - [workflow] [varchar](8000) NULL, - [ownerid] [varchar](8000) NULL, - [createdbyname] [varchar](8000) NULL, - [createdon] [datetime2](6) NULL, - [duration] [bigint] NULL, - [endtime] [datetime2](6) NULL, - [errorcode] [varchar](8000) NULL, - [errormessage] [varchar](8000) NULL, - [flowrunid] [varchar](8000) NULL, - [name] [varchar](8000) NULL, - [owneridname] [varchar](8000) NULL, - [parentrunid] [varchar](8000) NULL, - [PartitionId] [varchar](8000) NULL, - [resourceid] [varchar](8000) NULL, - [starttime] [datetime2](6) NULL, - [status] [varchar](8000) NULL, - [triggertype] [varchar](8000) NULL, - [ttlinseconds] [bigint] NULL, - [workflowid] [varchar](8000) NULL, - [workflowname] [varchar](8000) NULL, - [TenantId] [varchar](8000) NULL, - [EnvironmentId] [varchar](8000) NULL, - [EnvironmentName] [varchar](8000) NULL, - [Region] [varchar](8000) NULL, - [ReferenceDate] [date] NULL - ) - GO - -``` - -### Create DesktopFlowRun table for desktop flow runs - -```sql - CREATE TABLE DesktopFlowRuns - ( - [statecode] [bigint] NULL, - [statuscode] [bigint] NULL, - [runmode] [bigint] NULL, - [runsessionmode] [bigint] NULL, - [triggertype] [bigint] NULL, - [createdby] [varchar](8000) NULL, - [machinegroupid] [varchar](8000) NULL, - [machinegroupid_entitytype] [varchar](8000) NULL, - [machineid] [varchar](8000) NULL, - [machineid_entitytype] [varchar](8000) NULL, - [modifiedby] [varchar](8000) NULL, - [owningbusinessunit] [varchar](8000) NULL, - [owningteam] [varchar](8000) NULL, - [owninguser] [varchar](8000) NULL, - [regardingobjectid] [varchar](8000) NULL, - [regardingobjectid_entitytype] [varchar](8000) NULL, - [ownerid] [varchar](8000) NULL, - [additionalcontext] [varchar](8000) NULL, - [completedon] [datetime2](6) NULL, - [connectionid] [varchar](8000) NULL, - [context.parentWorkflowId] [varchar](8000) NULL, - [context.parentWorkflowRunId] [varchar](8000) NULL, - [context.attendedMode] [varchar](8000) NULL, - [context.connectionType] [varchar](8000) NULL, - [context.parentWorkflowType] [varchar](8000) NULL, - [correlationid] [varchar](8000) NULL, - [createdbyname] [varchar](8000) NULL, - [createdon] [datetime2](6) NULL, - [errorcode] [varchar](8000) NULL, - [errordetails] [varchar](8000) NULL, - [errorinnererror] [varchar](8000) NULL, - [errormessage] [varchar](8000) NULL, - [flowsessionid] [varchar](8000) NULL, - [gateway] [varchar](8000) NULL, - [inputs] [varchar](8000) NULL, - [machinegroupidname] [varchar](8000) NULL, - [machineidname] [varchar](8000) NULL, - [machinepercentcpuusage] [real] NULL, - [machinepercentramusage] [real] NULL, - [machineramusage] [bigint] NULL, - [modifiedbyname] [varchar](8000) NULL, - [modifiedon] [datetime2](6) NULL, - [name] [varchar](8000) NULL, - [outputs] [varchar](8000) NULL, - [owneridname] [varchar](8000) NULL, - [owneridtype] [varchar](8000) NULL, - [owningbusinessunitname] [varchar](8000) NULL, - [parentcloudflowrunsequenceid] [varchar](8000) NULL, - [parentworkflowid] [varchar](8000) NULL, - [processversion] [varchar](8000) NULL, - [regardingobjectidname] [varchar](8000) NULL, - [regardingobjecttypecode] [varchar](8000) NULL, - [rundetails] [varchar](8000) NULL, - [runduration] [bigint] NULL, - [runexecutionduration] [bigint] NULL, - [runwaitduration] [bigint] NULL, - [sessionusername] [varchar](8000) NULL, - [sessionusersid] [varchar](8000) NULL, - [startedon] [datetime2](6) NULL, - [IsDelete] [bit] NULL, - [PartitionId] [varchar](8000) NULL, - [TenantId] [varchar](8000) NULL, - [EnvironmentId] [varchar](8000) NULL, - [EnvironmentName] [varchar](8000) NULL, - [Region] [varchar](8000) NULL, - [ReferenceDate] [date] NULL - ) - GO -``` - -Your warehouse workspace should look similar to this now. -:::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-warehouse-table-creation-result.png" alt-text="Screenshot of a Fabric warehousing workspace with the list of tables that were created as part of this guide." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-warehouse-table-creation-result.png"::: - -## Manage default semantic model - -1. Ensure that you're in the Fabric warehousing workspace you used to generate the tables above and select the **Reporting** tab on top. -2. Select **Manage default semantic model** from the command bar, expand the tree node **dbo**, select all 4 tables and then **Confirm**. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png"::: - -## Create new semantic model - -1. Next, select **New semantic model** to create a new model we'll be using for reporting in Power BI later in this guide. -2. Give your semantic model a name i.e. `Automation Telemetry Model`, select all 4 tables again from the list and then **Confirm**. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/manage-default-semantic-model.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/new-semantic-model.png"::: -3. Once the data modelling experiences opens, select the **Dates** table and click on the three dots '...' next to the table name and select **Mark as date table**. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/mark-as-date-table.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/mark-as-date-table.png"::: -4. Now, define the relationships by dragging and dropping related fields from their child tables to their parent based as outlined below. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/semantic-model-erd.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/semantic-model-erd.png"::: - - | Source table | Source column | Target table | Target column | Details | - |--------------------|-----------------------|--------------|------------------|--------------------------------------------| - | CloudFlowRuns | ReferenceDate | Dates | Date | CloudFlowRuns.ReferenceDate --> Dates.Date | - | CloudFlowRuns | workflowid | Flows | workflowid | CloudFlowRuns.workflowid --> Flows.workflowid | - | DesktopFlowRuns | ReferenceDate | Dates | Date | DesktopFlowRuns.ReferenceDate --> Dates.Date | - | DesktopFlowRuns | regardingobjectid | Flows | workflowid | DesktopFlowRuns.regardingobjectid --> Flows.workflowid | - - Here's the outcome of the semantic model relationships we just defined. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png" alt-text="Screenshot of a Fabric manage default semantic model table selection experience." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-semantic-model-relationships.png"::: - -## What's next - -> [!div class="nextstepaction"] -> [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) - -## Learn more - -- [Automation-centric data analytics with Fabric](automation-analytics-with-fabric-introduction.md) -- [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) -- [Building custom Power BI reports for data insights in Fabric](automation-analytics-with-fabric-reports.md) -- [Setting up real-time alerts with Reflex for proactive monitoring](automation-analytics-with-fabric-alerts.md) \ No newline at end of file From c67d64a97b1675119a0e3263252db6869d7771bb Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 19 Dec 2024 10:20:08 +0100 Subject: [PATCH 34/44] Update automation-analytics-with-fabric-queries.md --- ...utomation-analytics-with-fabric-queries.md | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 23ceb0dcd..0d0911f95 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -47,10 +47,10 @@ The image includes only relations to tables that are included above and automati Step-by-step instructions to create a sample SQL Query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. -1. Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com) and log in with your credentials. -2. Choose the workspace where your Lakehouse is located and click on the desired SQL Analytical Endpoint (a sub-node of your Lakehouse). -3. Once in the SQL Analytical Endpoint, locate and click on the "New SQL query" button to open the SQL query editor interface. -4. In the SQL query editor, enter your SQL query and click **Run**. The following example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine Id that have failed within the last 7 days. +1. Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com) and sign in with your credentials. +2. Choose the workspace where your Lakehouse is located and select on the desired SQL Analytical Endpoint (a subnode of your Lakehouse). +3. Once in the SQL Analytical Endpoint, locate and select on the "New SQL query" button to open the SQL query editor interface. +4. In the SQL query editor, enter your SQL query and select **Run**. The following example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine ID that have failed within the last seven days. ```sql SELECT @@ -86,7 +86,7 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End | Succeeded | 4 | | Skipped | 5 | | Suspended | 6 | - | Cancelled | 7 | + | Canceled | 7 | | Failed | 8 | | Faulted | 9 | | TimedOut | 10 | @@ -96,8 +96,8 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End 6. Review the query results to ensure they meet your requirements. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of a SQL query being executed in a database management tool." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: 7. (Optional) You can open a Live-query with results in Excel by highlighting the SQL query and selecting "Open in Excel" in the query output section. This will generate and download an Excel file with a Live-query to the SQL Analytics endpoint to further deep-dive on the results. - :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of a SQL query being executed in a database query panel in Farbic." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: -8. (Optional) To store the SQL query for future use, click on the "Save Query" button. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of a SQL query being executed in a database query panel in Fabric." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: +8. (Optional) To store the SQL query for future use, select on the "Save Query" button. ### Basic flow queries @@ -150,7 +150,7 @@ This query returns all desktop flows with their owner information. ### Performance-related query example -This query retrieves the minimum, mean (average), maximum, and standard deviation of runtimes for desktop flow runs (Flow Sessions) of a specified desktop flow, with the runtimes converted from milliseconds rounded up to the nearest full second. The results are grouped by machine IDs and include additional details such as machine names, management types, maximum hosted machine counts, session capacity and the last heartbeat date etc. from the Machine Group and Machine tables. +This query retrieves the minimum, mean (average), maximum, and standard deviation of runtimes for desktop flow runs (Flow Sessions) of a specified desktop flow, with the runtimes converted from milliseconds rounded up to the nearest full second. The results are grouped by machine IDs and include other details such as machine names, management types, maximum hosted machine counts, session capacity, and the last heartbeat date etc. from the Machine Group and Machine tables. ```sql SELECT @@ -246,7 +246,7 @@ This query finds all desktop flows that use (OLEDB) DB connection strings that h #### Potential sensitive data loss or exfiltration risk -This query detects desktop flows that include scripting actions leveraging SAP's internal GUI scripting engine. If this is unexpected, it may pose risks of data loss or exfiltration, as sensitive information could be accessed or manipulated by these scripts and unintentionally or maliciously altered or exposed. +This query detects desktop flows that include scripting actions using SAP's internal GUI scripting engine. If this is unexpected, it may pose risks of data loss or exfiltration, as sensitive information could be accessed or manipulated by these scripts and unintentionally or maliciously altered or exposed. ```sql SELECT @@ -268,7 +268,7 @@ This query detects desktop flows that include scripting actions leveraging SAP's #### Potential SQL injection risk -This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](./desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (e.g., *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. +This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](./desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an 'Execute SQL statement' configuration dialog in Power Automate desktop." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: @@ -396,7 +396,7 @@ This query searches for desktop flows that lack any error handling mechanisms, s #### Desktop flow runs with restricted URL access -This query finds web service invocations (Invoke Web Service action) within a specific desktop flow over the past three weeks. This is particularly useful for identifying and analyzing potentially suspicious endpoints or restricted API calls. +This query finds web service invocations (Invoke Web Service action) within a specific desktop flow over the past three weeks. This is useful for identifying and analyzing potentially suspicious endpoints or restricted API calls. ```sql SELECT @@ -429,9 +429,9 @@ This query finds web service invocations (Invoke Web Service action) within a sp ); ``` -#### Desktop flow runs with cryprtographic code +#### Desktop flow runs with cryptographic code -This query scans desktop flow runs for PowerShell script actions that included cryptographic code over the past 7 days. +This query scans desktop flow runs for PowerShell script actions that included cryptographic code over the past seven days. ```sql -- Queries actions logs named 'Run PowerShell script' that contain code that that uses cryptographic libraries @@ -458,7 +458,7 @@ This query scans desktop flow runs for PowerShell script actions that included c #### Desktop flow runs with pro-code usage -This query is a bit more advanced. It identifies and counts the number of distinct desktop flow runs (Flow Sessions) that include pro-coding parts (such as VBScript, PowerShell, JavaScript, .NET, or Python) within the last 7 days, and then groups the results bydesktop flow. +This query is a bit more advanced. It identifies and counts the number of distinct desktop flow runs (Flow Sessions) that include pro-coding parts (such as VBScript, PowerShell, JavaScript, .NET, or Python) within the last seven days, and then groups the results by desktop flow. ```sql WITH ProCodingSessions AS ( @@ -529,7 +529,7 @@ ORDER BY f.ProCodingSessionCount DESC; #### Top 10 failing desktop flow actions -This query returns the top 10 failing actions by number of errors over the past 7 days. +This query returns the top 10 failing actions by number of errors over the past seven days. ```sql SELECT TOP(10) From d49d4fa4c7cec678d0836f06402c250bc02e40d4 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 19 Dec 2024 10:22:01 +0100 Subject: [PATCH 35/44] Update automation-analytics-with-fabric-introduction.md --- articles/automation-analytics-with-fabric-introduction.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 7c8a95b6b..c2d756c22 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -38,7 +38,7 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy ## Get started -In this guide, we'll walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you'll have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. +In this guide, we walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. ## Prerequisites @@ -47,11 +47,11 @@ Before you continue, ensure you meet the following prerequisites: 1. Have a basic understanding of data modeling, Power Automate, Dataverse, and the Fabric ecosystem. 2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. 3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. -4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you will see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. +4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you'll see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "Dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: -5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This will help others quickly identify the specific automations and data being processed in the Lakehouse. +5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This helps others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: -6. (Optional) Link additional Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. +6. (Optional) Link other Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. 7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## What's next From 9cb304b45cc8f515728d903c8d4c47a732fd7984 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 19 Dec 2024 10:28:39 +0100 Subject: [PATCH 36/44] Update automation-analytics-with-fabric-queries.md --- articles/automation-analytics-with-fabric-queries.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 0d0911f95..a453d8c2f 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -15,7 +15,7 @@ search.audienceType: - enduser --- -## Crafting automation-related queries with Fabric +# Crafting automation-related queries with Fabric > [!NOTE] > @@ -30,7 +30,7 @@ The following table includes automation-related tables that are frequently used | [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | | [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | | [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | -| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowlog | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](./desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | +| [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowlog | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](/power-automate/desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | | [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | @@ -268,7 +268,7 @@ This query detects desktop flows that include scripting actions using SAP's inte #### Potential SQL injection risk -This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](./desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. +This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/power-automate/desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an 'Execute SQL statement' configuration dialog in Power Automate desktop." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: @@ -392,7 +392,7 @@ This query searches for desktop flows that lack any error handling mechanisms, s > [!NOTE] > -> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. +> Before you continue with this section, ensure that [**Desktop Flow Logs V2**](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in your environment and that you have existing desktop flow runs. #### Desktop flow runs with restricted URL access From 4afc24360f040f42ba8a86256f48607972310764 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 19 Dec 2024 10:31:14 +0100 Subject: [PATCH 37/44] Update automation-analytics-with-fabric-introduction.md --- articles/automation-analytics-with-fabric-introduction.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index c2d756c22..1a6626768 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -52,7 +52,7 @@ Before you continue, ensure you meet the following prerequisites: 5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This helps others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: 6. (Optional) Link other Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](./desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. ## What's next From 4c66a5b5e7bab41718cb2f7822b004fe56e3d011 Mon Sep 17 00:00:00 2001 From: Apostolis Papaioannou Date: Thu, 19 Dec 2024 10:40:10 +0100 Subject: [PATCH 38/44] Update automation-analytics-with-fabric-introduction.md --- articles/automation-analytics-with-fabric-introduction.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 1a6626768..b5212be2b 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -40,6 +40,10 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy In this guide, we walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. +> [!NOTE] +> +> As per [this documentation](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric), it might take up to **60 minutes** to update data in OneLake including the conversion to delta parquet format. If you selected a table that contains a lot of data, the initial load time might take longer. When you open Fabric lakehouse, the links appear as **unidentified** until the initial sync is completed. More information: [Troubleshooting common issues](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#troubleshooting-common-issues) + ## Prerequisites Before you continue, ensure you meet the following prerequisites: From faad7f1f7a6d930e323c9ea304a58848d6eea3b6 Mon Sep 17 00:00:00 2001 From: DanaMartens Date: Thu, 19 Dec 2024 18:32:35 -0600 Subject: [PATCH 39/44] small changes --- ...tion-analytics-with-fabric-introduction.md | 24 +++++------ ...utomation-analytics-with-fabric-queries.md | 41 +++++++++---------- 2 files changed, 32 insertions(+), 33 deletions(-) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index b5212be2b..3596cb043 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -2,9 +2,9 @@ title: Automation-centric data analytics with the Dataverse - Fabric integration description: Learn how to configure Dataverse shortcuts in Microsoft Fabric to build advanced automation-related queries, reports, and alerts at scale. ms.topic: conceptual -ms.date: 11/06/2024 +ms.date: 12/19/2024 ms.author: appapaio -ms.reviewer: +ms.reviewer: dmartens contributors: author: rpapostolis ms.collection: conceptual @@ -17,13 +17,13 @@ search.audienceType: # Automation-centric data analytics with Fabric -Power Automate provides a comprehensive built-in monitoring and troubleshooting experience that allows organizations to manage their day-to-day operations, view trends, and access recommendations through features like the [Automation Center](automation-center-overview.md) and [Desktop Flow Activity](./desktop-flows/desktop-flow-activity.md). +Power Automate offers built-in monitoring and troubleshooting to help organizations manage operations, view trends, and access recommendations through features like the [Automation Center](automation-center-overview.md) and [Desktop Flow Activity](desktop-flow-activity.md). -However, your organization likely has more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. This is one of the key strengths of the Power Platform; through its native integrations with other low-code tooling and platforms, such as Power BI, Microsoft Fabric, as well as with Azure, organizations can develop highly sophisticated, scalable, and compliant analytics solutions using data from Power Automate and many other sources. +However, your organization likely has more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. One key strength of the Power Platform is its native integrations with other low-code tools and platforms like Power BI, Microsoft Fabric, and Azure. Organizations can develop sophisticated, scalable, and compliant analytics solutions using data from Power Automate and other sources. ## Microsoft Fabric as your analytics platform -[Microsoft Fabric](/fabric/get-started/microsoft-fabric-overview) is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a unified suite of analytical services including Data Engineering, Data Science, Real-Time Analytics, Data Warehousing with advanced data governance and security control. +[Microsoft Fabric](/fabric/get-started/microsoft-fabric-overview) is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a unified suite of analytical services, including data engineering, data science, real-time analytics, and data warehousing with advanced data governance and security control. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png" alt-text="Infographic showing different components of the Fabric data platform." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-toolset.png"::: @@ -38,25 +38,25 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy ## Get started -In this guide, we walk you through the process of using Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the foundational skills and architectural understanding needed to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to seamlessly monitor your data at scale. +This guide explains how to use Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the skills to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to monitor your data at scale. > [!NOTE] > -> As per [this documentation](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric), it might take up to **60 minutes** to update data in OneLake including the conversion to delta parquet format. If you selected a table that contains a lot of data, the initial load time might take longer. When you open Fabric lakehouse, the links appear as **unidentified** until the initial sync is completed. More information: [Troubleshooting common issues](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#troubleshooting-common-issues) +> According to [this documentation](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric), it can take up to **60 minutes** to update data in OneLake, including the conversion to delta parquet format. If you select a table that contains a lot of data, the initial load time can take longer. When you open Fabric lakehouse, the links appear as **unidentified** until the initial sync is completed. More information: [Troubleshooting common issues](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#troubleshooting-common-issues) ## Prerequisites Before you continue, ensure you meet the following prerequisites: -1. Have a basic understanding of data modeling, Power Automate, Dataverse, and the Fabric ecosystem. +1. Understand data modeling, Power Automate, Dataverse, and the Fabric ecosystem. 2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. 3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. -4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After the environment has been linked, you'll see a Lakehouse, a semantic model, and an SQL analytics endpoint. These are prefixed with "Dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. +4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After linking the environment, you see a Lakehouse, a semantic model, and an SQL analytics endpoint. These items are prefixed with "Dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: -5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This helps others quickly identify the specific automations and data being processed in the Lakehouse. +5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This change helps others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: -6. (Optional) Link other Dataverse environments within the same geographical region to Fabric to create cross-environment analytical solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) has been enabled in that environment and you have existing desktop flow runs. +6. (Optional) Link other Dataverse environments within the same geographical region to Fabric to create cross-environment analytics solutions. +7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) is enabled in that environment and you have existing desktop flow runs. ## What's next diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index a453d8c2f..28d6f96c4 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -2,9 +2,9 @@ title: Crafting automation-related queries with Fabric description: Learn how to query automation-related Dataverse data in Microsoft Fabric ms.topic: conceptual -ms.date: 11/06/2024 +ms.date: 12/19/2024 ms.author: appapaio -ms.reviewer: +ms.reviewer: dmartens contributors: author: rpapostolis ms.collection: conceptual @@ -23,7 +23,7 @@ search.audienceType: ## List of automation-related tables -The following table includes automation-related tables that are frequently used for reporting and observability use cases. +The following table lists automation-related tables frequently used for reporting and observability. | Display name | Object name | Purpose | |--------------------|------------------|-------------------------------------------------------| @@ -39,18 +39,18 @@ The following table includes automation-related tables that are frequently used ### Simplified table relationship diagram -The image includes only relations to tables that are included above and automation-relevant. +The image shows only the relevant table relations for automation. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png" alt-text="Screenshot of an entity relationship drawing showing automation-related table relations." lightbox="media/advanced-automation-related-data-analytics-fabric/automation-related-table-relations.png"::: ### Create your first query in Fabric -Step-by-step instructions to create a sample SQL Query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. +Follow these steps to create a sample SQL query on the SQL Analytical Endpoint in Fabric for the `contoso_westus_accounts_payable` Lakehouse. -1. Launch your web browser and navigate to the Microsoft Fabric portal (https://powerbi.com) and sign in with your credentials. -2. Choose the workspace where your Lakehouse is located and select on the desired SQL Analytical Endpoint (a subnode of your Lakehouse). -3. Once in the SQL Analytical Endpoint, locate and select on the "New SQL query" button to open the SQL query editor interface. -4. In the SQL query editor, enter your SQL query and select **Run**. The following example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine ID that have failed within the last seven days. +1. Open your web browser, go to the Microsoft Fabric portal (https://powerbi.com), and sign in with your credentials. +1. Select the workspace where your Lakehouse is located, then select the desired SQL Analytical Endpoint (a subnode of your Lakehouse). +1. In the SQL Analytical Endpoint, select **New SQL query** to open the SQL query editor. +1. In the SQL query editor, enter your SQL query and select **Run**. The following example query retrieves all desktop flow runs (flow sessions) associated with a specific desktop flow and a machine ID that failed within the last seven days. ```sql SELECT @@ -75,8 +75,8 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End ORDER BY createdon DESC; ``` - -5. Here's a list of available status reasons (statuscode) for the `Flow Sessions` (desktop flow runs) table. + +1. Here's a list of available status reasons (statuscode) for the `Flow Sessions` (desktop flow runs) table. | Status reason | Value | |-----------|-------| @@ -93,11 +93,11 @@ Step-by-step instructions to create a sample SQL Query on the SQL Analytical End | Aborted | 11 | | Ignored | 12 | -6. Review the query results to ensure they meet your requirements. +1. Review the query results to ensure they meet your needs. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of a SQL query being executed in a database management tool." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: -7. (Optional) You can open a Live-query with results in Excel by highlighting the SQL query and selecting "Open in Excel" in the query output section. This will generate and download an Excel file with a Live-query to the SQL Analytics endpoint to further deep-dive on the results. +1. (Optional) Open a Live-query with results in Excel by highlighting the SQL query and selecting **Open in Excel** in the query output section. This generates and downloads an Excel file with a Live-query to the SQL Analytics endpoint for further analysis. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of a SQL query being executed in a database query panel in Fabric." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: -8. (Optional) To store the SQL query for future use, select on the "Save Query" button. +1. (Optional) To store the SQL query for future use, select the **Save Query**. ### Basic flow queries @@ -150,7 +150,7 @@ This query returns all desktop flows with their owner information. ### Performance-related query example -This query retrieves the minimum, mean (average), maximum, and standard deviation of runtimes for desktop flow runs (Flow Sessions) of a specified desktop flow, with the runtimes converted from milliseconds rounded up to the nearest full second. The results are grouped by machine IDs and include other details such as machine names, management types, maximum hosted machine counts, session capacity, and the last heartbeat date etc. from the Machine Group and Machine tables. +This query retrieves the minimum, mean (average), maximum, and standard deviation of runtimes for desktop flow runs (Flow Sessions) of a specified desktop flow, with the runtimes converted from milliseconds rounded up to the nearest full second. The query groups the results by machine IDs and includes details such as machine names, management types, maximum hosted machine counts, session capacity, and the last heartbeat date from the Machine Group and Machine tables. ```sql SELECT @@ -215,14 +215,13 @@ This query identifies machine and licensing-related capacity issues for a specif f.machineid, fm.name, mg.managementtype, mg.maxmanagedmachinecount, fm.lastheartbeatdate, fm.sessioncapacity, fm.overcapacitysince ORDER BY capacity_status DESC, fm.lastheartbeatdate DESC; - ``` ### Governance-related query examples for desktop flows #### Find scripts that include plain text passwords in connections -This query finds all desktop flows that use (OLEDB) DB connection strings that have been configured to use a plaintext password. +This query finds all desktop flows that use (OLEDB) database connection strings that are configured to use a plaintext password. ```sql SELECT @@ -246,7 +245,7 @@ This query finds all desktop flows that use (OLEDB) DB connection strings that h #### Potential sensitive data loss or exfiltration risk -This query detects desktop flows that include scripting actions using SAP's internal GUI scripting engine. If this is unexpected, it may pose risks of data loss or exfiltration, as sensitive information could be accessed or manipulated by these scripts and unintentionally or maliciously altered or exposed. +This query detects desktop flows that include scripting actions using SAP's internal GUI scripting engine. If this is unexpected, these scripts might pose risks of data loss or exfiltration, as they could access or manipulate sensitive information and unintentionally or maliciously alter or expose it. ```sql SELECT @@ -268,7 +267,7 @@ This query detects desktop flows that include scripting actions using SAP's inte #### Potential SQL injection risk -This query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/power-automate/desktop-flows/actions-reference/database#executesqlstatement), the script is configured to use a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) that is provided to the script during runtime. This could pose a significant SQL injection risk. +The query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/power-automate/desktop-flows/actions-reference/database#executesqlstatement), the script uses a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) provided to the script during runtime. Such a scenario could pose a significant SQL injection risk. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an 'Execute SQL statement' configuration dialog in Power Automate desktop." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: @@ -396,7 +395,7 @@ This query searches for desktop flows that lack any error handling mechanisms, s #### Desktop flow runs with restricted URL access -This query finds web service invocations (Invoke Web Service action) within a specific desktop flow over the past three weeks. This is useful for identifying and analyzing potentially suspicious endpoints or restricted API calls. +This query finds web service invocations (Invoke Web Service action) within a specific desktop flow over the past three weeks. This result is useful for identifying and analyzing potentially suspicious endpoints or restricted API calls. ```sql SELECT @@ -458,7 +457,7 @@ This query scans desktop flow runs for PowerShell script actions that included c #### Desktop flow runs with pro-code usage -This query is a bit more advanced. It identifies and counts the number of distinct desktop flow runs (Flow Sessions) that include pro-coding parts (such as VBScript, PowerShell, JavaScript, .NET, or Python) within the last seven days, and then groups the results by desktop flow. +This query is a bit more advanced. It identifies and counts distinct desktop flow runs (Flow Sessions) with pro-coding parts (such as VBScript, PowerShell, JavaScript, .NET, or Python) from the last seven days, and groups the results by desktop flow. ```sql WITH ProCodingSessions AS ( From 02388677fe76beb464b79b8fba1f670fd3c933c9 Mon Sep 17 00:00:00 2001 From: DanaMartens Date: Fri, 20 Dec 2024 09:08:47 -0600 Subject: [PATCH 40/44] fix link --- articles/automation-analytics-with-fabric-introduction.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 3596cb043..0ef339b24 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -17,7 +17,7 @@ search.audienceType: # Automation-centric data analytics with Fabric -Power Automate offers built-in monitoring and troubleshooting to help organizations manage operations, view trends, and access recommendations through features like the [Automation Center](automation-center-overview.md) and [Desktop Flow Activity](desktop-flow-activity.md). +Power Automate offers built-in monitoring and troubleshooting to help organizations manage operations, view trends, and access recommendations through features like the [Automation Center](automation-center-overview.md) and [Desktop Flow Activity](desktop-flows/desktop-flow-activity.md). However, your organization likely has more advanced or custom monitoring, reporting, and analysis needs that aren't covered within the product today. One key strength of the Power Platform is its native integrations with other low-code tools and platforms like Power BI, Microsoft Fabric, and Azure. Organizations can develop sophisticated, scalable, and compliant analytics solutions using data from Power Automate and other sources. From b0fb35e6ad4814dd5ad4741d62547cc9209b22b6 Mon Sep 17 00:00:00 2001 From: DanaMartens Date: Fri, 20 Dec 2024 09:42:05 -0600 Subject: [PATCH 41/44] changes requested by Apostolis --- ...utomation-analytics-with-fabric-queries.md | 24 +------------------ 1 file changed, 1 insertion(+), 23 deletions(-) diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 28d6f96c4..dde581add 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -35,7 +35,7 @@ The following table lists automation-related tables frequently used for reportin | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | | [Work Queue Item](/power-apps/developer/data-platform/reference/entities/workqueueitem) | workqueueitem | Contains information about each run of a workflow.| -| [User](/power-apps/developer/data-platform/reference/entities/systemuser) | systemuser | Represents an instance of a workflow execution. | +| [User](/power-apps/developer/data-platform/reference/entities/systemuser) | systemuser | Represents the Dataverse user. | ### Simplified table relationship diagram @@ -243,28 +243,6 @@ This query finds all desktop flows that use (OLEDB) database connection strings AND (LOWER(w.definition) LIKE '%;password=%'); ``` -#### Potential sensitive data loss or exfiltration risk - -This query detects desktop flows that include scripting actions using SAP's internal GUI scripting engine. If this is unexpected, these scripts might pose risks of data loss or exfiltration, as they could access or manipulate sensitive information and unintentionally or maliciously alter or expose it. - -```sql - SELECT - workflowid AS 'Desktop flow Id', - name AS 'Desktop flow', - definition AS 'Script' - FROM - workflow - WHERE - (definition LIKE '%System.RunVBScript%' - OR definition LIKE '%System.RunPowershellScript%' - OR definition LIKE '%System.RunJavascript%' - OR definition LIKE '%System.RunDotNetScript%' - OR definition LIKE '%System.RunPythonScript%') - AND definition LIKE '%SapGuiAuto.GetScriptingEngine%' - ORDER BY - workflowid; -``` - #### Potential SQL injection risk The query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/power-automate/desktop-flows/actions-reference/database#executesqlstatement), the script uses a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) provided to the script during runtime. Such a scenario could pose a significant SQL injection risk. From 5424d2b7c7388695db236cee526155f3717abdc1 Mon Sep 17 00:00:00 2001 From: DanaMartens Date: Fri, 20 Dec 2024 10:20:46 -0600 Subject: [PATCH 42/44] small changes after review with Apostolis --- articles/TOC.yml | 2 +- ...utomation-analytics-with-fabric-introduction.md | 14 +++++++------- .../automation-analytics-with-fabric-queries.md | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/articles/TOC.yml b/articles/TOC.yml index 9de33cf2d..5bf00e819 100644 --- a/articles/TOC.yml +++ b/articles/TOC.yml @@ -1135,7 +1135,7 @@ items: - name: Overview href: automation-analytics-with-fabric-introduction.md - - name: Crafting automation-centric ad-hoc queries + - name: Create ad-hoc queries href: automation-analytics-with-fabric-queries.md - name: Capacity utilization items: diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 0ef339b24..f0d2c93c2 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -38,7 +38,7 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy ## Get started -This guide explains how to use Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the skills to build sophisticated data queries, generate insightful reports, design interactive dashboards, and set up proactive alerts to monitor your data at scale. +This guide explains how to use Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the skills to understand automation-centric data models, build sophisticated data queries in order to generate insightful reports. > [!NOTE] > @@ -49,14 +49,14 @@ This guide explains how to use Dataverse shortcuts within Microsoft Fabric to cr Before you continue, ensure you meet the following prerequisites: 1. Understand data modeling, Power Automate, Dataverse, and the Fabric ecosystem. -2. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. -3. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. -4. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After linking the environment, you see a Lakehouse, a semantic model, and an SQL analytics endpoint. These items are prefixed with "Dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. +1. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. +1. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. +1. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After linking the environment, you see a Lakehouse, a semantic model, and an SQL analytics endpoint. These items are prefixed with "Dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: -5. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This change helps others quickly identify the specific automations and data being processed in the Lakehouse. +1. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This change helps others quickly identify the specific automations and data being processed in the Lakehouse. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: -6. (Optional) Link other Dataverse environments within the same geographical region to Fabric to create cross-environment analytics solutions. -7. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) is enabled in that environment and you have existing desktop flow runs. +1. (Optional) Link other Dataverse environments within the same geographical region to Fabric to create cross-environment analytics solutions. +1. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) is enabled in that environment and you have existing desktop flow runs. ## What's next diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index dde581add..95e543d72 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -245,7 +245,7 @@ This query finds all desktop flows that use (OLEDB) database connection strings #### Potential SQL injection risk -The query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/power-automate/desktop-flows/actions-reference/database#executesqlstatement), the script uses a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) provided to the script during runtime. Such a scenario could pose a significant SQL injection risk. +The query detects desktop flows that contain scripts potentially vulnerable to SQL injection by searching for the use of `database.executesqlstatement.execute` within the flow definitions. Consider a scenario where, instead of directly writing the SQL code in the [Execute SQL statement action](/power-automate/desktop-flows/actions-reference/database#executesqlstatement), the script uses a Power Automate desktop input variable (for example, *%LetsDeleteAllGeneralLedgerEntriesFromDB%*) provided to the script during runtime. :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png" alt-text="Screenshot of an 'Execute SQL statement' configuration dialog in Power Automate desktop." lightbox="media/advanced-automation-related-data-analytics-fabric/execute-sql-statmenet-risk.png"::: From d08e4fe7328357b3efd54cc855b379cce18898da Mon Sep 17 00:00:00 2001 From: DanaMartens Date: Fri, 20 Dec 2024 16:17:43 -0600 Subject: [PATCH 43/44] small changes --- ...tion-analytics-with-fabric-introduction.md | 10 +++++++--- ...utomation-analytics-with-fabric-queries.md | 20 +++++++++++-------- 2 files changed, 19 insertions(+), 11 deletions(-) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index f0d2c93c2..47cb97048 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -38,7 +38,7 @@ The Link to Microsoft Fabric feature built into Power Platform makes all your Dy ## Get started -This guide explains how to use Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the skills to understand automation-centric data models, build sophisticated data queries in order to generate insightful reports. +This guide explains how to use Dataverse shortcuts within Microsoft Fabric to create powerful data analytics solutions. By the end of this guide, you have the skills to understand automation-centric data models, and build sophisticated data queries to generate insightful reports. > [!NOTE] > @@ -51,10 +51,14 @@ Before you continue, ensure you meet the following prerequisites: 1. Understand data modeling, Power Automate, Dataverse, and the Fabric ecosystem. 1. Have access to a Dataverse environment (in the same region as your Fabric capacity) with a Power Automate premium license, (ideally) existing cloud, and desktop flow runs, and System Administrator role in this environment. 1. Ensure that you meet the [**prerequisites**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#prerequisites) before linking your Dataverse environment with Fabric. -1. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After linking the environment, you see a Lakehouse, a semantic model, and an SQL analytics endpoint. These items are prefixed with "Dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. +1. Follow [**these steps**](/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric#link-to-microsoft-fabric) to link your Dataverse environment with Microsoft Fabric. After linking the environment, you see a Lakehouse, a semantic model, and a SQL analytics endpoint. These items are prefixed with "Dataverse," followed by your environment name and a unique environment suffix, such as `dataverse_contosousap_cds2_workspace_unq111111111111111111111111`. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png" alt-text="Screenshot of Fabric workspace showing a Dataverse-related lakehouse and other artifacts." lightbox="media/advanced-automation-related-data-analytics-fabric/fabric-workspace-with-lakehouse.png"::: -1. (Optional) Select Lakehouse settings and rename your Lakehouse to a more meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This change helps others quickly identify the specific automations and data being processed in the Lakehouse. + +1. (Optional) Select Lakehouse settings and rename your Lakehouse to a meaningful name, such as "contoso_westus_accounts_payable," and provide a brief description. This change helps others quickly identify the specific automations and data processed in the Lakehouse. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png" alt-text="Screenshot of the Fabric interface with details such as name, description, location, and ownership of a lakehouse." lightbox="media/advanced-automation-related-data-analytics-fabric/lakehouse-renaming.png"::: + 1. (Optional) Link other Dataverse environments within the same geographical region to Fabric to create cross-environment analytics solutions. 1. (Optional) If you plan to follow the advanced section for [Desktop flow action log-level analytics](automation-analytics-with-fabric-queries.md#governance-related-query-examples-for-v2-action-logs), ensure that [**Desktop Flow Logs V2**](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version) is enabled in that environment and you have existing desktop flow runs. diff --git a/articles/automation-analytics-with-fabric-queries.md b/articles/automation-analytics-with-fabric-queries.md index 95e543d72..5bb174d33 100644 --- a/articles/automation-analytics-with-fabric-queries.md +++ b/articles/automation-analytics-with-fabric-queries.md @@ -1,5 +1,5 @@ --- -title: Crafting automation-related queries with Fabric +title: Create automation-related queries with Fabric description: Learn how to query automation-related Dataverse data in Microsoft Fabric ms.topic: conceptual ms.date: 12/19/2024 @@ -15,11 +15,11 @@ search.audienceType: - enduser --- -# Crafting automation-related queries with Fabric +# Create automation-related queries with Fabric > [!NOTE] > -> **Disclaimer:** The scenarios, query examples, and data used in this tutorial are fictional, may include errors, inefficiencies and are intended solely for demonstration purposes. +> **Disclaimer:** The scenarios, query examples, and data used in this tutorial are fictional, may include errors, inefficiencies, and are intended solely for demonstration purposes. ## List of automation-related tables @@ -27,15 +27,15 @@ The following table lists automation-related tables frequently used for reportin | Display name | Object name | Purpose | |--------------------|------------------|-------------------------------------------------------| -| [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | -| [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | -| [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | | [Flow Log](/power-apps/developer/data-platform/reference/entities/flowlog) | flowlog | Contains a wide variety of logs, such as custom logs, [desktop flow action logs V2](/power-automate/desktop-flows/configure-desktop-flow-logs#configure-desktop-flow-action-log-version), [machine run queue logs](/power-automate/desktop-flows/monitor-run-details#view-queue-events), unattended self-heal requests/responses, and work queue processing logs etc. The data is stored in a [Dataverse elastic table](/power-apps/maker/data-platform/create-edit-elastic-tables), and depending on the log type, can be configured with its own [time-to-live (TTL)](/power-apps/developer/data-platform/elastic-tables#expire-data-by-using-time-to-live) setting in the [Organization table](/power-apps/developer/data-platform/reference/entities/organization#writable-columnsattributes) ([FlowLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_FlowLogsTtlInMinutes) and [DesktopFlowQueueLogsTtlInMinutes](/power-apps/developer/data-platform/reference/entities/organization#BKMK_DesktopFlowQueueLogsTtlInMinutes)), which defines when records should be automatically deleted from the table. | | [Flow Machine](/power-apps/developer/data-platform/reference/entities/flowmachine) | flowmachine | Contains machine and hosted machine-related info. | | [Flow Machine Group](/power-apps/developer/data-platform/reference/entities/flowmachinegroup) | flowmachinegroup | Contains machine group and hosted machine group-related info. | +| [Flow Run](/power-apps/developer/data-platform/reference/entities/flowrun) | flowrun | Contains cloud flow run-related data such start, end, duration, parent flow context etc. | +| [Flow Session](/power-apps/developer/data-platform/reference/entities/flowsession) | flowsession | Contains desktop flow run-related data such as start, durations, status, machine, robot account, parent flow context etc. | +| [Process](/power-apps/developer/data-platform/reference/entities/workflow) | workflow | Contains desktop flows and solution-based cloud flows (along with other workflow types). | +| [User](/power-apps/developer/data-platform/reference/entities/systemuser) | systemuser | Represents the Dataverse user. | | [Work Queue](/power-apps/developer/data-platform/reference/entities/workqueue) | workqueue | Represents an instance of a workflow execution. | | [Work Queue Item](/power-apps/developer/data-platform/reference/entities/workqueueitem) | workqueueitem | Contains information about each run of a workflow.| -| [User](/power-apps/developer/data-platform/reference/entities/systemuser) | systemuser | Represents the Dataverse user. | ### Simplified table relationship diagram @@ -94,10 +94,14 @@ Follow these steps to create a sample SQL query on the SQL Analytical Endpoint i | Ignored | 12 | 1. Review the query results to ensure they meet your needs. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png" alt-text="Screenshot of a SQL query being executed in a database management tool." lightbox="media/advanced-automation-related-data-analytics-fabric/basic-sql-flowsession-query.png"::: + 1. (Optional) Open a Live-query with results in Excel by highlighting the SQL query and selecting **Open in Excel** in the query output section. This generates and downloads an Excel file with a Live-query to the SQL Analytics endpoint for further analysis. + :::image type="content" source="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png" alt-text="Screenshot of a SQL query being executed in a database query panel in Fabric." lightbox="media/advanced-automation-related-data-analytics-fabric/open-query-in-excel.png"::: -1. (Optional) To store the SQL query for future use, select the **Save Query**. + +1. (Optional) To store the SQL query for future use, select **Save Query**. ### Basic flow queries From 8b51e9b74de10428fb127d44f7aa558488be6c43 Mon Sep 17 00:00:00 2001 From: DanaMartens Date: Fri, 20 Dec 2024 16:22:15 -0600 Subject: [PATCH 44/44] update one word in next steps section --- articles/automation-analytics-with-fabric-introduction.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/automation-analytics-with-fabric-introduction.md b/articles/automation-analytics-with-fabric-introduction.md index 47cb97048..adf8026e9 100644 --- a/articles/automation-analytics-with-fabric-introduction.md +++ b/articles/automation-analytics-with-fabric-introduction.md @@ -65,4 +65,4 @@ Before you continue, ensure you meet the following prerequisites: ## What's next > [!div class="nextstepaction"] -> [Crafting automation-related queries with Fabric](automation-analytics-with-fabric-queries.md) +> [Create automation-related queries with Fabric](automation-analytics-with-fabric-queries.md)

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