In Spark 2.0, we have extended DataFrames and Datasets in Spark to handle streaming data. Streaming Datasets not only provides a single programming abstraction for batch and streaming data, it brings support for event-time based processing, out-or-order/delayed data, sessionization and tight integration with non-streaming data sources and sinks. In this talk, I will take a deep dive into the concepts and the API and show how this simplifies building complex “continuous applications”.
Tathagata Das is an Apache Spark Committer and a member of the PMC. He’s the lead developer behind Spark Streaming, and is currently employed at Databricks. Before Databricks, you could find him at the AMPLab of UC Berkeley, researching about datacenter frameworks and networks with professors Scott Shenker and Ion Stoica.