Apache Spark 2.0.

Apache Spark 2.0.

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Bibliographic Details
Meeting name:	Strata Conference and Hadoop World (2016 : San Jose, California)
Imprint:	[Place of publication not identified] : O'Reilly Media, 2016.
Description:	1 online resource (1 streaming video file (1 hr., 11 min., 50 sec.)) : digital, sound, color
Language:	English
Subject:	Spark (Electronic resource : Apache Software Foundation) Spark (Electronic resource : Apache Software Foundation) Data mining. Data Mining Data mining.
Format:	E-Resource Video
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13645302

Hidden Bibliographic Details
Other authors / contributors:	Armbrust, Michael, on-screen presenter. Das, Tathagata, on-screen presenter.
Notes:	Title and publication information from resource description page (Safari, viewed May 24, 2016). Presenters, Michael Armbrust and Tathagata Das.
Summary:	"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 developed."--Resource description page.

Table of Contents:

Apache Spark and real-time analytics : from interactive queries to streaming / [Michael Armbrust]
Taking Spark streaming to the next level with dataframes / [Tathagata Das].