databricks-spark-structured-streaming

Production-ready Spark Structured Streaming pipelines use patterns for Kafka ingestion, Delta Lake sinks, multi-sink writes, watermarks, and state management to ensure reliable streaming workloads.

Cost optimization in Spark Structured Streaming involves configuring triggers, managing state stores efficiently, and applying watermark strategies to control resource consumption during processing.

Triggers and watermarks in Spark Streaming define processing time boundaries and handle late data, which configures state management for reliable event-time processing across pipelines.

Spark Structured Streaming supports Change Data Capture scenarios and Bronze/Silver/Gold medallion architectures through stream-static joins and multi-sink write patterns.

Spark Structured Streaming supports stream-stream joins and multi-sink writes, providing comprehensive patterns to route and aggregate data across multiple downstream targets.

Checkpoints in Kafka to Delta Lake streaming pipelines maintain state and recovery information, preventing data loss and duplication during job restarts or failures.