databricks-spark-structured-streaming

To build a real-time streaming pipeline from Kafka to Delta Lake, use Spark Structured Streaming to ingest event data, apply transformations, and write results to Delta Lake tables while handling late-arriving data with watermarks.

Spark Structured Streaming handles stateful operations by configuring watermarks to drop late data and using RocksDB state stores to manage large state efficiently, ensuring robust aggregations and joins in production pipelines.

Yes, you can join a streaming DataFrame with a static Delta table using stream-static joins to enrich real-time event data with dimension tables without requiring the static source to be streamed.

Optimize Spark Structured Streaming performance and cost by selecting appropriate triggers, configuring cluster resources efficiently, and applying best practices for state management and Delta Lake merges to reduce overhead.

Perform merge operations in a streaming pipeline by writing to Delta Lake and using the merge pattern to upsert data, allowing you to update existing records and insert new ones efficiently within Structured Streaming.

You need watermarks in Spark Structured Streaming when handling stateful operations like aggregations or stream-stream joins, as they define the threshold for accepting late data and allow the engine to drop old state.