databricks-spark-structured-streaming

Build a Spark Structured Streaming pipeline by configuring Kafka ingestion, applying watermarks for late data, setting checkpointing, and writing to a Delta sink with tuned triggers. This guide provides reference implementations for reliable real-time processing.

Stateful stream-stream joins in Spark Structured Streaming require watermarking both input streams to bound state size and manage late data. This skill details patterns for stream-static and stream-stream joins to ensure correct aggregation.

Checkpointing in Spark Structured Streaming persists state and offsets to durable storage, enabling fault recovery and exactly-once processing. You need it to prevent data loss and duplicate writes during pipeline failures.

Yes, Spark Structured Streaming supports multi-sink writes by splitting a streaming DataFrame into multiple output paths. This skill covers patterns for distributing real-time data to different downstream systems concurrently.

Tune Spark Structured Streaming triggers by adjusting processing time intervals or using continuous mode for sub-millisecond latency. This guide offers cost-aware tuning strategies to balance resource allocation and real-time processing latency.

Yes, Spark Structured Streaming integrates natively with Delta Lake for real-time mode operations, supporting exactly-once writes and schema enforcement. The skill provides quick start examples for Delta sink configurations.