databricks-spark-structured-streaming

To build a Spark Structured Streaming pipeline, configure a read stream from your Kafka source, apply real-time data processing transformations, and write the output stream to a Delta table.

Handle stateful operations in Spark Structured Streaming by defining watermarks to drop late data and configuring state stores to manage and optimize the state retention required for real-time data processing.

Yes, you need existing Spark and Structured Streaming knowledge to effectively utilize this guidance, as it focuses on building, optimizing, and managing production-ready streaming pipelines rather than teaching basic concepts.

The best way to optimize performance for real-time data processing in Spark is by properly configuring watermarks, managing state stores for stateful operations, and applying production-ready pipeline construction techniques.

You should use watermarks in a streaming pipeline when handling stateful operations, as they define the threshold for late data and prevent unbounded state growth by dropping outdated events during real-time data processing.