From 285e11298ee72a00292c2f614d4d745b4ac6df7c Mon Sep 17 00:00:00 2001 From: srjhunjhunwalacorp <64076463+srjhunjhunwalacorp@users.noreply.github.com> Date: Tue, 5 Dec 2023 16:30:11 +0530 Subject: [PATCH] Blog OPC UA to Delta Lake (#604) * blog opcua Signed-off-by: S.jhunjhunwala2@shell.com * OPC UA to delta Signed-off-by: S.jhunjhunwala2@shell.com * author update Signed-off-by: S.jhunjhunwala2@shell.com * picture update opc ua blog Signed-off-by: S.jhunjhunwala2@shell.com * updates Signed-off-by: Chloe Ching * updates Signed-off-by: Chloe Ching --------- Signed-off-by: S.jhunjhunwala2@shell.com Signed-off-by: Chloe Ching Co-authored-by: Chloe Ching --- docs/blog/.authors.yml | 4 ++ docs/blog/images/opcua-to-deltalake.png | Bin 0 -> 152731 bytes docs/blog/posts/opcua_to_delta.md | 65 ++++++++++++++++++++++++ 3 files changed, 69 insertions(+) create mode 100644 docs/blog/images/opcua-to-deltalake.png create mode 100644 docs/blog/posts/opcua_to_delta.md diff --git 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z0wd%}$<8<%kBRDzq!MA}bhp8n!g=Zg3Cs}Nm$19Ci<1^r#Ulr?-opR=P(Tsojfw)6 z)OZ~Bjrp9J`fxXP6mqPhwOy}*v%~E9J;Hu<9GX;bs6>sfA$OwjnVN}-3zVN%E>ZlC zWS2Fb-pPRf>b1*CWkUY=D)S!||6A@yjPzOJ?+~`YC5Zplec-tuq4Rvc5r($^UW@;H z@Kx}#wh0Hc?e1T`c%I}QiRLzA`p>^=`SSfc8sUX<=%`Qs^F(0GIMUY(LTc|$36}98 zklxb1oLl^qOcE62zwh~RF#Rvx0)J=#BGvRgS2jK+lPMs5{I1+z^aTiimqgP&`?qKQ zDVd4^>7_R8^-c-@2@vV-=T*~FGCc*P2hH>+oD%*DAkwE>m=ean%G3x*kNlDQ|E|z= Zy6w6H?YsK>J?DU*J30p1g_=)a{TH4Z + +## What Are the Alternatives? + +Hyperscalers have become a central part of global IT infrastructure and essential to operations and they also started providing industrial connectivity options out of many in this article I am going to focus on Azure , OPC UA & Open source stack which provides you all the options of data ingestion from OPC UA source to your cloud database. + +![blog](../images/opcua-to-deltalake.png){width=100%} + + +## Components + +## Microsoft OPC Publisher as Interface + +OPC Publisher is a Microsoft-endorsed solution that acts as a vital link between industrial equipment and the Azure cloud infrastructure. Its core function involves linking OPC UA-enabled devices or industrial connectivity software to your Azure cloud environment, transmitting collected telemetry data to Azure IoT Hub in multiple formats, one of which is the standardized IEC62541 OPC UA PubSub format (from version 2.6 onwards). + +OPC Publisher is versatile in its deployment options, running either as an Azure IoT Edge Module or a Docker container on various platforms, thanks to its compatibility with the .NET cross-platform runtime. Notably, it can seamlessly operate on both Linux and Windows 10 systems. + +Refer to [Microsoft OPC Publisher documentation](https://learn.microsoft.com/en-us/azure/industrial-iot/tutorial-publisher-deploy-opc-publisher-standalone) for more information. + +## RTDIP for Data Engineering + +The Real-Time Data Ingestion Platform (RTDIP) is designed to offer convenient access to large volumes of historical and live operational data for analytical purposes, serving engineers and data scientists no matter where they are located. + +### Key Components of RTDIP: + +#### Delta Ingestion Engine: +This component operates in the cloud and is responsible for processing time series data sourced from various streaming endpoints such as Eventhub, Kafka, and others, as well as data from files. It ingests and organizes this data into a Delta Lakehouse. + +To simplify the data ingestion from IoT hub, RTDIP has built Transformer components to convert the payload's defaultmessage encoding to json. The architecture above is using the transformer [OPCPublisherOPCUAJsonToPCDMTransformer](https://www.rtdip.io/sdk/pipelines/components/#:~:text=OPC%20Publisher%20OPCUA%20Json%20To%20Process%20Control%20Data%20Model) to handle that scenario. + +#### Query Engine +Python SDK: The Python Software Development Kit (SDK) empowers users to interact with the data stored within the Delta Lakehouse. It provides a programmatic interface for working with the data. + +#### REST APIs +In addition to the Python SDK, RTDIP offers RESTful Application Programming Interfaces (APIs) that deliver the same capabilities as the Python SDK. These APIs enable users to interact +and manipulate the data stored in the Delta Lakehouse through HTTP requests, making it accessible for a wide range of applications and services. + +See [Real Time Data Ingestion Platform](https://www.rtdip.io/) for more information. + +## Visualization + +Real-time data visualization is a dynamic and essential component of data analytics and monitoring processes. It involves the presentation of data as it is generated or updated in real-time, allowing users to gain instant insights and make informed decisions. But for visualization there is always different requirement, in current open source scenario we have different options available which can be used as required: + +- [Plotly](https://plotly.com/) +- [Dash](https://plotly.com/dash/) +- [Grafana](https://grafana.com/docs/grafana/latest/) + +## Conclusion + +To conclude this article, it is important to emphasize the wide array of open-source options at the disposal of historians. In today’s market, numerous open-source components can be harnessed to create bespoke solutions that seamlessly link with your process control systems, enabling the integration of real-time data into modern data platforms. While this article focused on Azure as an illustration, it’s worth noting that AWS offers similar capabilities, providing flexibility and adaptability to cater to your specific needs. \ No newline at end of file