by O'Reilly Media, Inc. | Released May 2016 | ISBN: 9781491963975

In March 2016 at Strata in San Jose, CA, a standing room only audience of excited developers heard the first public overview of the dramatic changes coming to Apache Spark. Listen and watch as Databrick presenters Michael Armbrust and Tathagata Das run through the breakthrough concepts and technologies driving the Structured Streaming capabilities in Spark 2.0. Get a first-look preview of the break-through changes coming to Structured Streaming in 2.0 Understand the unified input/out API that works with virtually any format (JSON, Parquet, etc.) Learn about Datasets – a new abstraction that eliminates large swaths of unnecessary code Get how 2.0 simplifies exploration of large data stores and ensures error-free production pipelines Learn about the Catalyst optimizer and Tungsten – new tools for efficient pipeline analysis Explore an end-to-end execution pipeline that allows difficult ad-hoc interactive queries and more Learn about streaming DataFrames and how they unify interactive analysis See a demo of Structured Streaming and learn why it was developedMichael Arbrust and Tathagata Das work at Databricks, the company founded by the creators of Apache Spark. Das is the lead developer behind Spark Streaming, which he started while a PhD student in the UC Berkeley AMPLab. Armbrust is the lead developer for Spark's SQL project. He holds a PhD from Berkeley. Show and hide more Publisher resources View/Submit Errata

1. Apache Spark and Real-time Analytics: From Interactive Queries to Streaming
   • Achieving Real-time Analytics - Michael Armbrust (Databricks) 00:01:26
   • Develop Productively with Simple APIs - Michael Armbrust (Databricks) 00:04:01
   • Datasets with SQL - Michael Armbrust (Databricks) 00:00:58
   • Dynamic Datasets (DataFrames) - Michael Armbrust (Databricks) 00:00:53
   • Static Datasets - Michael Armbrust (Databricks) 00:01:31
   • Unified, Structured APIs - Michael Armbrust (Databricks) 00:03:30
   • Execute Efficiently: The Catalyst Optimizer and Tungsten Engine - Michael Armbrust (Databricks) 00:04:51
   • Operating Directly on Serialized Data - Michael Armbrust (Databricks) 00:03:01
   • Understanding Encoders - Michael Armbrust (Databricks) 00:01:37
   • Streaming: Updating Automatically - Michael Armbrust (Databricks) 00:06:08
   • Demo: Structured Streaming in Spark 2.0 - Michael Armbrust (Databricks) 00:10:13
2. Taking Spark Streaming to the Next Level with DataFrames
   • Streaming in Spark - Tathagata Das (Databricks) 00:05:35
   • Lessons from: Pain Points with DStreams - Tathagata Das (Databricks) 00:02:01
   • Building Structured Streaming - Tathagata Das (Databricks) 00:06:16
   • Continuous Aggregations - Tathagata Das (Databricks) 00:02:10
   • Execution - Tathagata Das (Databricks) 00:01:33
   • Advantages over DStreams - Tathagata Das (Databricks) 00:07:13
   • Stateful Stream Processing - Tathagata Das (Databricks) 00:06:35
   • Plan for Spark 2.0 - Tathagata Das (Databricks) 00:02:18
Show and hide more