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

Lead Developer of Spark Streaming | Databricks

Tathagata Das is an Apache Spark committer and a member of the PMC. He’s the lead developer behind Spark Streaming and currently develops Structured Streaming. Previously, he was a grad student in the UC Berkeley at AMPLab, where he conducted research about data-center frameworks and networks with Scott Shenker and Ion Stoica.

Tathagata Das

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