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Here are some recommendations on how to run Spark Streaming on Synapse for real-time production projects, considering the limitations of Spark job definitions:
- Structured Streaming in Synapse Spark: This is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows ingesting real-time data from various data sources, including Event Hubs. You can create a new notebook in Synapse, initialize the variables, and set up your source by providing the name of your Event Hub along with the connection string and consumer group. Your destination will be Data Lake, so you need to supply the container and folder path where you want to land the streaming data.
refer :https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/structured-streaming-in-synapse-spark/ba-p/3692836 - Azure Synapse Analytics with a dedicated Spark pool: You can configure a dedicated Spark pool in your Azure Synapse workspace. This allows you to run Spark jobs, including streaming jobs, for an indefinite period. Here are some resources to get you started:
- [Create a dedicated Spark pool (version 3.2 or above) for Apache Spark in Azure Synapse Analytics workspace]https://learn.microsoft.com/en-us/azure/templates/microsoft.synapse/workspaces/bigdatapools
- [Ingest and process real-time data streams with Azure Synapse Analytics]https://learn.microsoft.com/en-us/sql/big-data-cluster/spark-streaming-guide?view=sql-server-ver15
- HDInsight Spark Activity: If you want to run Spark jobs continually, you can use HDInsight. However, to make the pipeline run continually, you might need to set up a mechanism to restart the job once it finishes.
Remember, the choice between these options depends on your specific use case and requirements. It’s also important to note that managing streaming data effectively often involves a combination of these techniques.
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