
Spark - The Definitive Guide
Big data processing made simple
O'Reilly (Publisher)
Published on 31. March 2018
Book
Paperback/Softback
450 pages
978-1-4919-1221-8 (ISBN)
Description
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.
You'll explore the basic operations and common functions of Spark's structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark's scalable machine-learning library.
Get a gentle overview of big data and Spark
Learn about DataFrames, SQL, and Datasets-Spark's core APIs-through worked examples
Dive into Spark's low-level APIs, RDDs, and execution of SQL and DataFrames
Understand how Spark runs on a cluster
Debug, monitor, and tune Spark clusters and applications
Learn the power of Structured Streaming, Spark's stream-processing engine
Learn how you can apply MLlib to a variety of problems, including classification or recommendation
You'll explore the basic operations and common functions of Spark's structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark's scalable machine-learning library.
Get a gentle overview of big data and Spark
Learn about DataFrames, SQL, and Datasets-Spark's core APIs-through worked examples
Dive into Spark's low-level APIs, RDDs, and execution of SQL and DataFrames
Understand how Spark runs on a cluster
Debug, monitor, and tune Spark clusters and applications
Learn the power of Structured Streaming, Spark's stream-processing engine
Learn how you can apply MLlib to a variety of problems, including classification or recommendation
More details
Language
English
Place of publication
Sebastopol
United States
Target group
Professional and scholarly
Dimensions
Height: 231 mm
Width: 177 mm
Thickness: 39 mm
Weight
1052 gr
ISBN-13
978-1-4919-1221-8 (9781491912218)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

E-Book
02/2018
O'Reilly
€50.49
Available for download

E-Book
02/2018
O'Reilly
€50.49
Available for download
Persons
Bill Chambers is a Product Manager at Databricks focusing on large-scale analytics, strong documentation, and collaboration across the organization to help customers succeed with Spark and Databricks. He has a Master's degree in Information Systems from the UC Berkeley School of Information, where he focused on data science. Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. He started the Spark project at UC Berkeley in 2009, where he was a PhD student, and he continues to serve as its vice president at Apache. Matei also co-started the Apache Mesos project and is a committer on Apache Hadoop. Matei's research work was recognized through the 2014 ACM Doctoral Dissertation Award and the VMware Systems Research Award.