Yes, you can process streaming data in Azure Synapse Analytics, but it is not directly achieved through Synapse pipelines or Synapse data flows for event-driven sources like Azure Event Hub. Synapse Pipelines and Data Flows are primarily built for batch processing rather than real-time streaming.
However, Azure Synapse Analytics supports real-time processing of streaming data through Azure Stream Analytics (ASA) jobs or Apache Spark pools within Synapse. Here's how you can set it up:
- Azure Stream Analytics Integration
You can use Azure Stream Analytics (ASA) within Synapse to process streaming data from Event Hub. This allows you to apply real-time queries to transform and analyze the data in motion, including joining with Cosmos DB or other data stores.
- Steps to set up:
- Configure an Event Hub as an input to Stream Analytics.
- Set up the necessary transformations and windowing functions (if required) within the Stream Analytics job.
- Send output data to Cosmos DB, SQL Pool, ADX, or even to storage for further analysis.
- Apache Spark Streaming in Synapse
Alternatively, you can use Apache Spark in Synapse for real-time streaming data processing from Event Hub. Spark Streaming allows you to continuously receive data from Event Hubs and join it with other sources, such as Cosmos DB.
- Steps to set up:
- Use a Spark pool in Synapse to connect to Event Hub.
- Write a Spark Streaming job using Spark APIs to consume data from Event Hub and perform transformations or joins with other data sources like Cosmos DB.
- The output can be written back to Cosmos DB, ADX, or other supported Synapse destinations.
Why can't you find Event Hub in Synapse Pipelines/Data Flows?
Currently, Synapse Pipelines and Data Flows do not natively support Event Hub as a source because they are designed primarily for batch data processing. While Synapse supports orchestration of streaming processes (for example, triggering Stream Analytics jobs), pipelines and data flows are not real-time data processing engines themselves.