Do your Streaming ETL at Scale with Apache Spark’s Structured Streaming

At the Spark Summit in San Francisco in June, we announced that Apache Spark’s Structured Streaming is marked as production-ready and shared benchmarks to demonstrate its performance compared to other streaming engines.

Structured Streaming is a novel way to process streams. Not only does this new way make it easy to build end-to-end streaming applications, but it also handles all the underlying complexities for fault-tolerance. You as a developer need not worry about it.

At the Data + AI Summit, I will present two talks covering many aspects of Structured Streaming. The first talk covers concepts, APIs, integration with external sources and sinks, underlying incremental Spark SQL execution engine, and fault-tolerant semantics, while the second will focus on stateful stream processing using mapGroupsWithState APIs.

  1. Easy, Scalable, fault-tolerant Stream Processing with Structured Streaming in Apache Spark
  2. Deep Dive into Stateful Streaming Processing in Structured Streaming

Why should you attend these sessions? If you are a data engineer or data scientist who wants to turbocharge your ETL with streaming, build low-latency predictive IoT or fraud-detection applications with fast-data, and create streaming pipelines for data ingestion and real-time streaming analytics, then attend my sessions.

And see you Dublin!

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