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Learning Spark / Holden Karau, Andy Konwinski, Patrick Wendell, and Matei Zaharia.

By: Karau, Holden [author.].
Contributor(s): Konwinski, Andy [author.] | Wendell, Patrick [author.] | Zaharia, Matei [author.].
Edition: First edition.Description: xvi, 254 pages : illustrations ; 24 cm.ISBN: 9781449358624; 1449358624.Subject(s): Spark (Electronic resource : Apache Software Foundation) | Big data | Data mining -- Computer programsDDC classification: 006.3/12 Online resources: Full-text here
Contents:
Introduction to data analysis with Spark -- Downloading Spark and getting started -- Programming with RDDs -- Working with key/value pairs -- Loading and saving your data -- Advanced Spark programming -- Running on a cluster -- Tuning and debugging Spark -- Spark SQL -- Spark streaming -- Machine learning with MLlib.
Summary: This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.--
List(s) this item appears in: Books 2014 - 2017
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Item type Current location Call number Status Date due Barcode Item holds
Book Skoltech library
Shelves
QA76.9.D343 K363 2015 (Browse shelf) Available 2000006361
Book Skoltech library
Shelves
QA76.9.D343 K363 2015 (Browse shelf) Available 2000006317
Total holds: 0

Subtitle on cover: Lightning-fast data analysis.

Includes index.

Introduction to data analysis with Spark -- Downloading Spark and getting started -- Programming with RDDs -- Working with key/value pairs -- Loading and saving your data -- Advanced Spark programming -- Running on a cluster -- Tuning and debugging Spark -- Spark SQL -- Spark streaming -- Machine learning with MLlib.

This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.--

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