From f51926615a04802c74297e1e04c283802a10ffe9 Mon Sep 17 00:00:00 2001 From: Mohanna Shahrad <59365092+mohannashahrad@users.noreply.github.com> Date: Wed, 7 Sep 2022 19:39:24 -0400 Subject: [PATCH 1/2] Fixed the OpenSearch Document type / missing image The OpenSearch CDK application's structure image was missed in the previous version of the guide. This commit fixed that typo. --- cloud_templates/user_guides/opensearch_guide.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/cloud_templates/user_guides/opensearch_guide.md b/cloud_templates/user_guides/opensearch_guide.md index 3e7c69d..f6d4f2d 100644 --- a/cloud_templates/user_guides/opensearch_guide.md +++ b/cloud_templates/user_guides/opensearch_guide.md @@ -47,7 +47,8 @@ If you are interested in using the CloudFormation templates more than just for d 3. Just to verify everything is working correctly, list the stacks in your app by running `cdk ls` command. If you don't see `OpensearchPatternStack`, make sure you are currently in `OpenSearchPattern` directory. 4. The structure of the files inside `OpenSearchPattern` is as below: -[Image: Screen Shot 2022-08-25 at 12.52.09 PM.png] + + * `open_search_pattern_stack.py` is the main code of the stack. It is here where the required resources are created. * `tests/unit/test_open_search_pattern_stack.py` is where the unit tests of the stack is written. The unit tests check * Right creation of the resources in addition to their properties From 0cc2a554ba4159e301059f9f716cbe4b5e562c7d Mon Sep 17 00:00:00 2001 From: Mohanna Shahrad <59365092+mohannashahrad@users.noreply.github.com> Date: Fri, 9 Sep 2022 15:34:43 -0400 Subject: [PATCH 2/2] Updated toplevel README with patterns' description --- cloud_templates/README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/cloud_templates/README.md b/cloud_templates/README.md index 5baade6..1fd9953 100644 --- a/cloud_templates/README.md +++ b/cloud_templates/README.md @@ -9,22 +9,32 @@ These templates are designed in four different patterns to ingest your IoT data
alt text

+In this structure, first the MQTT messages are captured in IoT Core and then pushed to Amazon Timestream by using IoT Core built rule which will provide a table view of the data to the customer. To create graphical visuals based on the data you can integrate Timestream with several visualization toools such as Amazon QuickSight. To learn more about the IoT Core-Timestream rule please see [here](https://docs.aws.amazon.com/iot/latest/developerguide/timestream-rule-action.html). + 2. AWS IoT Analytics
alt text

+In this structure, after capturing the MQTT messages, using IoT Core built in rule, data is sent to an AWS IoT Analytics channel. Then the data will be directed to the Analytics Datastore through the Analytics pipeline. To learn more about the IoT Core to IoT Analytics rule check [here](https://docs.aws.amazon.com/iot/latest/developerguide/iotanalytics-rule-action.html). IoT Analytics automates the steps required to analyze data from IoT devices. It can filter, transform, and enrich IoT data before storing it in a time-series data store for analysis. AWS IoT Analytics is available in the following [regions](https://aws.amazon.com/iot-analytics/pricing/). + 3. Amazon Kinesis Data Firehose
alt text

+In this pattern, the captured MQTT messages in AWS IoT core will be sent to an Amazon Kinesis Data Firehose allowing you to collect, process and analyze large bandwidth of data in real time. The service enables you to author and run code against streaming sources to perform time-series analytics, feed real-time dashboards, and create real-time metrics. You can create a Delivery Stream in the Kinesis Firehose with an S3 bucket as its destination in which the data will be stored. You can then visualize the data in the bucket by building an [Athena table](https://docs.aws.amazon.com/athena/latest/ug/data-sources-glue.html) (using Glue Crawler) on top of that and [connecting Athena to Amazon Quicksight](https://docs.aws.amazon.com/quicksight/latest/user/create-a-data-set-athena.html). Detailed information about the IoT Core rule for sending data to Kinesis Data Firehose is available [here](https://docs.aws.amazon.com/iot/latest/developerguide/kinesis-firehose-rule-action.html). Amazon Kinesis Firehose is available in the following [regions](https://aws.amazon.com/kinesis/data-firehose/pricing/). + 4. Amazon OpenSearch Service
alt text

+Amazon OpenSearch Service is a managed service that makes it easy to deploy, operate, and scale OpenSearch clusters in the AWS Cloud. It is a fully open-source search and analytics engine for use cases such as log analytics, real-time application monitoring, and clickstream analysis. To learn more you can refer to [What is Amazon OpenSearch Service?](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/) in the Amazon OpenSearch Service Developer Guide. +In this pattern, the captured data in IoT Core will be pushed to Amazon OpenSearch service through built in IoT rule. You can then use tools like OpenSearch integrated dashboards to query and visualize data in OpenSearch Service. For more details refer to [OpenSearch rule action](https://docs.aws.amazon.com/iot/latest/developerguide/opensearch-rule-action.html). + + The subdirectories contain the following: * Under `demo` directory, there are the demo templates along with a sample program to run on your Expresslink.