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Modern applications require the capability to retrieve modified data from a database in real time to operate effectively. Usually, developers need to create a customized tracking mechanism in their applications, utilizing triggers, timestamp columns, and supplementary tables, to identify changes in data. The development of such applications typically requires significant effort and can result in schema updates resulting in considerable performance overhead. 


Real-time data processing is a crucial aspect of nearly every modern data warehouse project. However, one of the biggest hurdles to overcome in real-time processing solutions is the ability to ingest efficiently and effectively, process, and store messages in real-time, particularly when dealing with high volumes of data. To ensure optimal performance, processing must be conducted in a manner that does not interfere with the ingestion pipeline. In addition to non-blocking processing, the data store must be capable of handling high-volume writes. Further challenges such as the ability to quickly act on the data, generating real-time alerts or business needs where dashboard that needs to be updated in real-time or near real-time. In many cases, the source systems utilize traditional relational database engines, such as MySQL, that do not offer event-based interfaces.  


In this series of blog posts, we will introduce an alternative solution that utilizes an open-source tool Debezium to perform Change Data Capture (CDC) from Azure Database for MySQL – Flexible Server with Apache Kafka writes these changes to the Azure Event Hub, Azure Stream Analytics perform real time analytics on the data stream and then write to Azure Data Lake Storage Gen2 for long-term storage and further analysis using Azure Synapse serverless SQL pools and provide insights through Power BI 


Azure Database for MySQL – Flexible Server is a cloud-based solution that provides a fully managed MySQL database service. This service is built on top of Azure’s infrastructure and offers greater flexibility. MySQL uses binary log (binlog) to record all the transactions in the order in which they are committed on the database. This includes changes to table schemas as well as changes to the rows in the tables. MySQL uses binlog mainly for purposes of replication and recovery. 


Debezium is a powerful CDC (Change Data Capture) tool that is built on top of Kafka Connect. It is designed to stream the binlog, produces change events for row-level INSERT, UPDATE, and DELETE operations in real-time from MySQL into Kafka topics, leveraging the capabilities of Kafka Connect. This allows users to efficiently query only the changes from the last synchronization and upload those changes to the cloud. After this data is stored in Azure Data Lake storage, it can be processed using Azure Synapse Serverless SQL Pools. Business users can then monitor, analyse, and visualize the data using Power BI.


Solution overview 

This solution entails ingesting MySQL data changes from the binary logs and converting the changed rows into JSON messages, which are subsequently sent to Azure Event Hub. After the messages are received by the Event Hub, an Azure Stream Analytics (ASA) Job distributes the changes into multiple outputs, as shown in the following diagram.

architecture mysql.jpg

End-to-end serverless streaming platform with Azure Event Hubs for data ingestion 


Components and Services involved

In this blog post, following are the services used for streaming the changes from Azure Database for MySQL to Power BI. 

  • A Microsoft Azure account 

  • An Azure Database for MySQL Flexible server  

  • A Virtual Machine running Linux version 20.04 

  • Kafka release (version 1.1.1, Scala version 2.11), available from 

  • Debezium 1.6.2 

  • An Event Hubs namespace 

  • Azure Stream Analytics 

  • Azure Data Lake Storage Gen2 

  • Azure Synapse Serverless SQL pools

  • A Power BI workspace 



The following steps outline the process to set up the components involved in this architecture to stream data in real time from the source Azure Database for MySQL flexible Server.

  1. Provisioning and configuring Azure Database for MYSQL- Flexible Server & a Virtual Machine 

  2. Configure and run Kafka Connect with a Debezium MySQL connector 

  3. Reading CDC Messages Downstream from Azure Event Hub and capture data in an Azure Data Lake Storage Gen2 account in Parquet format 

  4. Create External Table with Azure Synapse Serverless SQL Pool 

  5. Use Serverless SQL pool with Power BI Desktop & create a report. 

  6. Build real-time dashboard with Power BI dataset produced from Stream Analytics 

Each of the above steps is outlined in detail in the upcoming sections.  




Provisioning and configuring Azure Database for MYSQL- Flexible Server & a Virtual Machine 

It is important to create an Azure Database for MySQL Flexible Server instance and a Virtual Machine as outlined below before proceeding to the next step. To do so, perform the following steps: 

  1. Create an instance of Azure Database for MySQL – Flexible Server 

  2. Under server parameters blade, configure binlog_expire_logs_seconds parameter, as per your requirements (e.g.: 86400 seconds  for 24Hrs) on the server to make sure that binlogs are not purged quickly. For more information, see How to Configure server parameters. 

  3. Under the same server parameter blade, also configure and set binlog_row_image parameter to a value of FULL. 

  4. Use a command line client or download and install MySQL Workbench or another third-party MySQL client tool to connect to the Azure Database for MySQL Flexible Server. 

  5. Create an Azure VM in the same resource group running Linux version 20.04. 

  6. Maintain enough disk space on the Azure VM to copy binary logs remotely. 

  7. For this example, the “orders_info” table has been created in Azure Database for MySQL Flexible Serversaikondapalli_0-1681405946971.png


Configure and run Kafka Connect with a Debezium MySQL connector  

To track row-level changes in response to insert, update and delete operations in database tables, Change Data Capture (CDC) is a technique that you use to track these changes, Debezium is a distributed platform that provides a set of Kafka Connect connectors that can convert these changes into event streams and send those events to Apache Kafka.  


To set up Debezium & Kafka on a Linux Virtual Machine follow the steps outlined in: CDC in Azure Database for MySQL – Flexible Server using Kafka, Debezium, and Azure Event Hubs – Microsoft Community Hub 

Reading CDC Messages Downstream from Event Hub and capture data in an Azure Data Lake Storage Gen2 account in Parquet format

Azure Event Hubs is a fully managed Platform-as-a-Service (PaaS) Data streaming and Event Ingestion platform, capable of processing millions of events per second. Event Hubs can process, and store events, data, or telemetry produced by distributed software and devices. Data sent to an event hub can be transformed and stored by using any real-time analytics provider or batching/storage adapters. Azure Events Hubs provides an Apache Kafka endpoint on an event hub, which enables users to connect to the event hub using the Kafka protocol.  

Configure a job to capture data 

Use the following steps to configure a Stream Analytics job to capture data in Azure Data Lake Storage Gen2. 

  1. In the Azure portal, navigate to your event hub. 

  2. Select the event hub created for the “orders_info” table.

  3. Select Features > Process Data, and then select Start on the Capture data to ADLS Gen2 in Parquet format card. 





3. Enter a name to identify your Stream Analytics job. Select Create. 




4. Specify the Serialization type of your data in the Event Hubs and the Authentication method that the job will use to connect to Event Hubs. Then select Connect. 





5. Then the connection is established successfully, you’ll see: 

  • Fields that are present in the input data. You can choose Add field or you can select the three dots symbol next to a field to optionally remove, rename, or change its name. 

  • A live sample of incoming data in the Data preview table under the diagram view. It refreshes periodically. You can select Pause streaming preview to view a static view of the sample input. 






  1. Select the Azure Data Lake Storage Gen2 tile to edit the configuration. 

  2. On the Azure Data Lake Storage Gen2 configuration page, follow these steps: 

    a. Select the subscription, storage account name and container from the drop-down menu.
    b. After the subscription is selected, the authentication method and storage account key should be automatically filled in. 
    c. For streaming blobs, the directory path pattern is expected to be a dynamic value. It’s required for the date to be a part of the file path for the blob – referenced as {date}. To learn about custom path patterns, see to Azure Stream Analytics custom blob output partitioning. 


     d. Select Connect 

  1. When the connection is established, you’ll see fields that are present in the output data. 

  2. Select Save on the command bar to save your configuration. 

  3. On the Stream Analytics job page, under the Job Topology heading, select Query to open the Query editor window.  

  4. To test your query with incoming data, select Test query 

  5. After the events are sampled for the selected time range, they appear in the Input preview tab. 




  1. Stop the job before you make any changes to the query for any desired output. In many cases, your analysis doesn’t need all the columns from the input stream. You can use a query to project a smaller set of returned fields than in the pass-through query.

  2. When you make changes to your query, select Save query to test the new query logic. This allows you to iteratively modify your query and test it again to see how the output changes. 




  1. After you verify the results, you’re ready to Start the job. 

  2. Select Start on the command bar to start the streaming flow to capture data. Then in the Start Stream Analytics job window: 

  • Choose the output start time. 

  • Select the number of Streaming Units (SU) that the job runs with. SU represents the computing resources that are allocated to execute a Stream Analytics job. For more information, see Streaming Units in Azure Stream Analytics. 

  • In the Choose Output data error handling list, select the behavior you want when the output of the job fails due to data error. Select Retry to have the job retry until it writes successfully or select another option. 






  1. verify that the Parquet files are generated in the Azure Data Lake Storage container. 




Create External Table with Azure Synapse Serverless SQL Pool 


  1. Navigate to Azure Synapse Analytics Workspace. Select Data -> Linked -> Navigate to the ADLS gen 2 (folder path) 

  2. Select the file that you would like to create the external table from and right click -> New SQL Script -> Create External table  






3. In the New External Table, change Max string length to 250 and continue 



4. A dialog window will open. Select or create new database and provide database table name and select Open script 




5. A new SQL Script opens, and you run the script against the database, and it will create a new External table. 

6. Making a pointer to a specific file. You can only point to folder not the files too 

7. Point to enriched folder in Data Lake Storage 



8. Save all the work by clicking Publish All 

9. Verify the external table created in Data -> Workspace -> SQL Database 



External tables encapsulate access to files making the querying experience almost identical to querying local relational data stored in user tables. Once the external table is created, you can query it just like any other table:






SELECT TOP 100 * FROM dbo.orders_info 

SELECT COUNT(*) FROM dbo.orders_info 







10. END 


Use serverless SQL pool with Power BI Desktop & create a report

  1. Navigate to Azure Synapse Analytics Workspace. Starting from Synapse Studio, click Manage. 





 2. Under External Connections, click Linked services. Click + New. Click Power BI and click Continue. 






3. Enter a name for the linked service and select an existing workspace which you want to use to publish. Provide any name in the “Name” field. Then you will see Power BI linked connection with the name. 

4. Click Create. 





5. View Power BI workspace in Synapse Studio 

  • After your workspaces are linked, you can browse your Power BI datasets, edit/create new Power BI Reports from Synapse Studio. 

  • Navigate to develop hub. Create Power BI linked service will be here. 

  • Expand Power BI and the workspace you wish to use. 



6. New reports can be created clicking + at the top of the Develop tab. Existing reports can be edited by clicking on the report name. Any saved changes will be written back to the Power BI workspace. 






Overall, Debezium, Kafka Connect, Azure Event Hubs, Azure Data Lake Storage, Azure Stream Analytics, Synapse SQL Serverless, and Power BI work together to create a comprehensive, end-to-end data integration, analysis, and visualization solution that can handle real-time data streams from databases, store them in a scalable and cost-effective manner, and provide insights through a powerful BI tool.  

To learn more about the services used in this post, check out the following resources: 




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