databricks-spark-structured-streaming

To build a Spark Structured Streaming pipeline for Kafka to Delta Lake ingestion, you read from Kafka, parse the JSON payloads, and write to Delta with a configured checkpoint. This pattern provides reliable, real-time data ingestion in Databricks environments.

The best way to handle stateful processing and watermarking is configuring the state store with RocksDB and applying watermarking to manage late data. This ensures reliable stream-stream and stream-static joins without unbounded state growth.

To perform MERGE-based upserts in a medallion architecture, you use Delta MERGE optimizations within your streaming pipeline to update and insert records. This supports multi-sink writes for continuous data reconciliation.

Yes, Spark Structured Streaming works with both stream-static and stream-stream joins in Databricks. It applies watermarking and state management to ensure deterministic processing patterns and robust data merging.

You tune Spark Structured Streaming performance using Liquid Clustering and Delta MERGE optimizations to optimize file layout and query speed. These features reduce small file problems and accelerate downstream read performance.

You need checkpointing in Spark Structured Streaming pipelines to provide fault tolerance and exactly-once processing guarantees. It tracks the streaming query's progress and state information to recover seamlessly from failures.