databricks-spark-structured-streaming

Spark Structured Streaming handles stream-static joins by enriching live streaming data with static dimension tables, while stream-stream joins match records across two continuous streams using watermarks to bound state and manage late data.

Watermarks in Spark Structured Streaming define a maximum event latency threshold, allowing the engine to drop late data and aggressively clean up old state, which prevents unbounded state growth during stateful operations.

Trigger optimization for real-time data processing involves balancing micro-batch frequency against latency requirements, configuring processing time or available-now triggers to control batch size, and tuning cluster resources for Kafka ingestion throughput.

You can perform merge operations on streaming data within Delta Lake by applying foreachBatch logic to run upserts, enabling targeted updates to existing records rather than simple appends during continuous data integration.