
Learning Spark
Lightning-Fast Big Data Analysis
O'Reilly (Publisher)
1st Edition
Published on 13. February 2015
Book
Paperback/Softback
272 pages
978-1-4493-5862-4 (ISBN)
Article exhausted; check for reprint
Description
Data in all domains is getting bigger. How can you work with it efficiently? 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.
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
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
More details
Language
English
Place of publication
Sebastopol
United States
Target group
Professional and scholarly
Dimensions
Height: 233 mm
Width: 178 mm
ISBN-13
978-1-4493-5862-4 (9781449358624)
Schweitzer Classification
Other editions
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08/2020
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O'Reilly
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01/2015
1st Edition
O'Reilly
€35.99
Available for download

E-Book
01/2015
1st Edition
O'Reilly
€29.99
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Persons
Holden Karau is a software development engineer at Databricks and is active in open source. She is the author of an earlier Spark book. Prior to Databricks she worked on a variety of search and classification problems at Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelors of Mathematics in Computer Science. Outside of software she enjoys paying with fire, welding, and hula hooping. Most recently, Andy Konwinski co-founded Databricks. Before that he was a PhD student and then postdoc in the AMPLab at UC Berkeley, focused on large scale distributed computing and cluster scheduling. He co-created and is a committer on the Apache Mesos project. He also worked with systems engineers and researchers at Google on the design of Omega, their next generation cluster scheduling system. More recently, he developed and led the AMP Camp Big Data Bootcamps and first Spark Summit, and has been contributing to the Spark project. Matei Zaharia is a PhD student in the AMP Lab at UC Berkeley, working on topics in computer systems, cloud computing and big data. He is also a committer on Apache Hadoop and Apache Mesos. At Berkeley, he leads the development of the Spark cluster computing framework, and has also worked on projects including Mesos, the Hadoop Fair Scheduler, Hadoop's straggler detection algorithm, Shark, and multi-resource sharing. Matei got his undergraduate degree at the University of Waterloo in Canada.