databricks-spark-structured-streaming

To build a Spark Structured Streaming pipeline from Kafka to Delta Lake, you ingest data from Kafka sources and write outputs continuously to Delta Lake tables. This Skill provides patterns for configuring the Kafka source, applying transformations, and executing the continuous Delta write operation.

Spark Structured Streaming handles late-arriving data by defining watermarks that specify how long the engine waits for late data before finalizing stateful aggregations. This Skill covers best practices for configuring watermarks to manage state stores and drop outdated records.

Yes, you can perform stream-static joins in Databricks to enrich real-time streaming data with static dimension tables. This Skill outlines patterns for stream-static joins and stream-stream joins, allowing you to augment incoming records with supplementary context.

The best way to optimize triggers for Structured Streaming production workloads involves tuning trigger intervals and cluster configurations to balance latency and throughput. This Skill provides performance tuning patterns for triggers to ensure robust, production-ready stream processing.

You ensure exactly-once semantics in Spark Structured Streaming pipelines by utilizing checkpointing alongside idempotent sink writes to Delta Lake. This Skill addresses fault tolerance and checkpointing configurations required to guarantee exactly-once processing guarantees.