Using Structured Streaming in Apache Spark: Insights Without Tradeoffs

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Apache Spark 2.0 introduced Structured Streaming which allows users to continually and incrementally update your view of the world as new data arrives while still using the same familiar Spark SQL abstractions. Michael Armbrust from Databricks talks about the progress made since the release of Spark 2.0 on robustness, latency, expressiveness and observability, using examples of production end-to-end continuous applications.

Overview:
Parallelism and Complexity
Developer Productivity and Efficiency
Throughput and Latency
Production Use Cases
- Viacom
- iPass
Streaming at Databricks
Engineer Office Hours

This talk was originally presented at Spark Summit East 2017.

You can view the slides on Slideshare:
http://www.slideshare.net/databricks/spark-summit-east-2017-michael-armbrust-keynote-insights-without-tradeoffs-using-structured-streaming

Related Articles:
Structured Streaming In Apache Spark
https://databricks.com/blog/2016/07/28/structured-streaming-in-apache-spark.html

Real-time Streaming ETL with Structured Streaming in Apache Spark 2.1
https://databricks.com/blog/2017/01/19/real-time-streaming-etl-structured-streaming-apache-spark-2-1.html

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