Spark Streaming – The State of the Union and the Road Beyond

Spark Streaming extends the core Apache Spark API to perform large-scale stream processing, which is revolutionizing the way Big “Streaming” Data application are being written. It is rapidly adopted by companies spread across various business verticals – ad and social network monitoring, real-time analysis of machine data, fraud and anomaly detections, etc. These companies are mainly adopting Spark Streaming because – Its simple, declarative batch-like API makes large-scale stream processing accessible to non-scientists. – Its unified API and a single processing engine (i.e. Spark core engine) allows a single cluster and a single set of operational processes to cover the full spectrum of uses cases – batch, interactive and stream processing. – Its stronger, exactly-once semantics makes it easier to express and debug complex business logic. In this talk, I am going to elaborate on such adoption stories, highlighting interesting use cases of Spark Streaming in the wild. In addition, I am also going to talk about (and perhaps also demonstrate) the exciting new developments in Spark Streaming and the wish list of features that we may target in the future.

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About Tathagata Das

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.