databricks-spark-structured-streaming

Build a Kafka to Delta streaming pipeline by reading from Kafka topics, enriching events with Delta dimension tables, and writing to persistent checkpoint locations like Unity Catalog volumes to ensure exactly-once durability and fault recovery during stateful processing.

Handle stateful aggregations and stream-stream joins by configuring watermarks to manage late data and leveraging the RocksDB state store. This prevents state explosion while maintaining accurate results across complex event processing workflows.

Yes, Spark Structured Streaming supports CDC merges and multi-sink writes on Databricks. You can implement parallel MERGE patterns and write to bronze, silver, and gold sinks simultaneously within a single ForEachBatch operation.

Prevent duplicate records in Delta Lake by implementing idempotent writes using transaction versions and application IDs. This exactly-once durability mechanism ensures streaming data is not duplicated during pipeline restarts or failures.

Yes, the streaming patterns apply to Microsoft Fabric Real-Time Mode scenarios. You can tune triggers using processingTime, availableNow, and RTM configurations to optimize operational costs and processing latency across platforms.

Use Liquid Clustering for Delta streaming sinks to optimize query performance during continuous data ingestion. It replaces traditional partitioning and Z-Ordering, automatically adapting data layout as streaming volumes grow.