Distributed processing includes both finite jobs and continuously running processors. Batch tuning focuses on stages and data movement; streaming operations also have to preserve event-time behavior, message compatibility and processing state across deployments.
In this chapter
- Spark execution, Dataflow, shuffles, and watermarksCompare distributed processing engines and explain skew or late-event failures.
- Schema evolution in real-time pipelinesKafka/Pub/Sub schema versions, rolling upgrades, state migration, Dataflow replacement and sink compatibility.
- Spark SQL plans, partitioning, AQE and skewScans, aggregates, shuffles and ranking windows in Spark.
- Lakehouse query plans: pruning, pushdown and runtime evidenceInterpret scan metadata and compare it with files, bytes and stage metrics.