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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
- Sales Rank: #14756 in Books
- Published on: 2015-02-27
- Released on: 2015-02-17
- Original language: English
- Number of items: 1
- Dimensions: 9.19" h x .63" w x 7.00" l, .0 pounds
- Binding: Paperback
- 276 pages
About the Author
Holden Karau is transgender Canadian, and anactive open source contributor. When not in San Francisco working as asoftware development engineer at IBM's Spark Technology Center, Holdentalks internationally on Spark and holds office hours at coffee shops athome and abroad. She makes frequent contributions to Spark, specializing inPySpark and Machine Learning. Prior to IBM she worked on a variety ofdistributed, search, and classification problems at Alpine, Databricks,Google, Foursquare, and Amazon. She graduated from the University ofWaterloo with a Bachelor of Mathematics in Computer Science. Outside ofsoftware she enjoys playing with fire, welding, scooters, poutine, anddancing.
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.
Patrick Wendell is an engineer at Databricks as well as a Spark Committer and PMC member. In the Spark project, Patrick has acted as release manager for several Spark releases, including Spark 1.0. Patrick also maintains several subsystems of Spark's core engine. Before helping start Databricks, Patrick obtained an M.S. in Computer Science at UC Berkeley. His research focused on low latency scheduling for large scale analytics workloads. He holds a B.S.E in Computer Science from Princeton University
Matei Zaharia is the creator of Apache Spark and CTO at Databricks. He holds a PhD from UC Berkeley, where he started Spark as a research project. He now serves as its Vice President at Apache. Apart from Spark, he has made research and open source contributions to other projects in the cluster computing area, including Apache Hadoop (where he is a committer) and Apache Mesos (which he also helped start at Berkeley).
Most helpful customer reviews
34 of 36 people found the following review helpful.
Prose is well-written, but style is an impediment to learning. Should be called "Reviewing Spark," not "Learning Spark"
By Silverstein
The textual components are well-written. However, the book tends to gloss over examples, providing the same, or similar, snippets to what's on the web site instead of providing full, working examples. This was a poor choice for a "learning" title. Instead of being able to work through each example, I found myself having to scroll around trying to figure out what was missing from each snippet, and how to put together working code. Also, the book presents Scala, Java, and Python snippets throughout the early chapters, which is very distracting. I found myself having to mentally context-switch between the three languages instead of being able to following one all the way through. Would I buy it again? Maybe. There are just a few Spark books, and they're all pretty meh. You have to learn it somewhere, I guess.
23 of 24 people found the following review helpful.
Start here: Excellent reference for Spark
By Brian Castelli
I found this volume to be an excellent reference book for a Spark learner like me. I am a software developer, and several reviews suggested that this volume was too basic. I shouldn't have followed their advice. I bought an "advanced" book, instead, only to find myself left without material to fill in some important gaps. The information that is available on the Internet is great, but this book brings much of it together in one place. If you want to learn to think like a Spark programmer--*not* the same as thinking like a programmer--this is the place to begin.
13 of 14 people found the following review helpful.
Nice Headstart to Spark
By Sathya Narayanan
I feel this is a decent compilation of the resources available over the internet. That way, it reduces the time needed for getting started with Spark. This book is definitely suitable anyone new to Spark and Big Data Processing. But for someone who has already worked with Spark and faced some challenges, this may not be helpful.
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