Portable Stream and Batch Processing with Apache Beam

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Stream processing is increasingly relevant in today’s world of big data, thanks to the lower latency, higher-value results, and more predictable resource utilization afforded by stream processing engines. At the same time, without a solid understanding of the necessary building blocks, streaming can feel like a complex and subtle beast. It doesn’t have to be that way.

Join Davor Bonaci for a tour of stream processing concepts via a walkthrough of the easiest to use yet most sophisticated stream processing model on the planet, Apache Beam. You’ll explore a series of examples that help shed light on the important topics of windowing, watermarks, and triggers; observe firsthand the different shapes of materialized output made possible by the flexibility of the Beam streaming model; experience the portability afforded by Beam, as you work through examples using the runner of your choice (Apache Flink, Apache Spark, or Google Cloud Dataflow); and interact with engineers who have years of experience with massive-scale stream processing.

Requirements

•  Laptop.
• GitHub account.
• Any initial setup for the Beam execution engine of your choice (Flink, Spark, or Cloud Dataflow) already completed.
• Familiarity with the high-level concepts of distributed data processing, and familiarity with Flink, Spark, or Cloud Dataflow would be a plus.