A Comprehensive Guide to Using Delta Lake with Spark Structured Streaming

The text provides a comprehensive overview of using Delta Lake with Spark Structured Streaming for reading and writing. It covers topics such as using Delta tables as sources and sinks, limiting input rate, streaming change data capture (CDC), handling updates and deletes, specifying initial position, processing initial snapshot, metrics, append mode, complete mode, stream-static joins, upserts using foreachBatch, and idempotent table writes. It also includes examples and important considerations for each topic.

• Delta Lake is deeply integrated with Spark Structured Streaming, addressing limitations associated with streaming systems and files.
• It supports incrementally reading Delta tables, controlling micro-batch options, streaming change data capture, handling updates and deletes, specifying initial position, and processing initial snapshot.
• Delta Lake also allows writing data into a Delta table using append mode, complete mode, performing stream-static joins, upserts using foreachBatch, and achieving idempotent table writes.
• The text includes examples and important considerations for each topic.