Learning Spark: Lightning-Fast Big Data Analysis

ISBN-13: 9781449358624
ISBN-10: 1449358624
Edition: 1
Author: Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia
Publication date: 2015
Publisher: O'Reilly Media
Format: Paperback 276 pages
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Book details

ISBN-13: 9781449358624
ISBN-10: 1449358624
Edition: 1
Author: Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia
Publication date: 2015
Publisher: O'Reilly Media
Format: Paperback 276 pages

Summary

Acknowledged authors Holden Karau , Andy Konwinski , Patrick Wendell , Matei Zaharia wrote Learning Spark: Lightning-Fast Big Data Analysis comprising 276 pages back in 2015. Textbook and eTextbook are published under ISBN 1449358624 and 9781449358624. Since then Learning Spark: Lightning-Fast Big Data Analysis textbook was available to sell back to BooksRun online for the top buyback price of $ 4.44 or rent at the marketplace.

Description

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. 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.

  • Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell
  • Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib
  • Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm
  • Learn how to deploy interactive, batch, and streaming applications
  • Connect to data sources including HDFS, Hive, JSON, and S3
  • Master advanced topics like data partitioning and shared variables
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