Spark Structured Streaming
Unified batch and streaming engine built on Apache Spark
About Spark Structured Streaming
Spark Structured Streaming extends Spark SQL for incremental stream processing. Treats streaming data as continuously appending tables. Available on Databricks, EMR, and self-managed Spark clusters.
Best for
Best for teams already on Spark wanting to add streaming capabilities
Pros & Cons
Pros
- Unified with Spark SQL — same API for batch and streaming
- Strong ecosystem and community
- Available on all major cloud platforms
Cons
- Micro-batch latency (~100ms) — not true event-at-a-time
- Stateful processing less mature than Flink
- Resource-heavy for simple streaming use cases
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