databricks-spark-structured-streaming

To build a Spark Structured Streaming pipeline from Kafka to Delta Lake, you configure a Kafka source, apply transformations, and write the streaming data to a Delta table using structured streaming write streams.

Watermarking in Spark Structured Streaming handles late-arriving data by defining a threshold for data delay, allowing the engine to drop data that arrives later than the threshold and manage stateful operations effectively.

You can optimize Spark Structured Streaming triggers for low-latency processing on Databricks by configuring trigger intervals, tuning cluster configurations, and utilizing merge operations to achieve cost-effectiveness.

The best way to enrich streaming Kafka clickstream data with a Delta table is to join the streaming Kafka source with a static Delta table containing user profile information within your Spark Structured Streaming pipeline.

Spark Structured Streaming supports writing to multiple sinks simultaneously through multi-sink writes, allowing a single streaming pipeline to output aggregated metrics and raw data to different destinations concurrently.

Delta Lake merge operations in a Spark Structured Streaming pipeline update existing records and insert new ones, enabling efficient stateful operations and maintaining data consistency in your gold-layer Delta tables.