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Real-Time Data Pipelines Made Easy with Structured Streaming in Apache Spark

Tathagata Das | Databricks


Structured Streaming is the next generation of distributed, streaming processing in Apache Spark. Developers can write a query written in their language of choice (Scala/Java/Python/R) using powerful high-level APIs (DataFrames / Datasets / SQL) and apply that same query to both static datasets and streaming data. In case of streaming, Spark will automatically create an incremental execution plan that automatically handles late, out-of-order data and ensures end-to-end exactly-once fault-tolerance guarantees.
In this practical session, I will walk through a concrete streaming ETL example where – in less than 10 lines – you can read raw, unstructured data from Kafka data, transform it and write it out as a structured table ready for batch and ad-hoc queries on up-to-the-last-minute data. I will give a quick glimpse of advanced features like event-time based aggregations, stream-stream joins and arbitrary stateful operations.
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Tathagata Das

Software Engineer | Databricks

Tathagata is a committer and PMC to the Apache Spark project and a Software Engineer at Databricks. He is the lead developer of Spark Streaming, and now focuses primarily on Structured Streaming. Previously, he was a member of the AMPLab, UC Berkeley as a graduate student researcher where he conducted research on data-center frameworks and networks with Scott Shenker and Ion Stoica.

Tathagata Das
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