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Learning Spark: Lightning-Fast Big Data Analysis Paperback – Feb 27 2015

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Product Details

  • Paperback: 276 pages
  • Publisher: O'Reilly Media; 1 edition (Feb. 27 2015)
  • Language: English
  • ISBN-10: 1449358624
  • ISBN-13: 978-1449358624
  • Product Dimensions: 17.8 x 1.6 x 23.3 cm
  • Shipping Weight: 476 g
  • Average Customer Review: 5.0 out of 5 stars 1 customer review
  • Amazon Bestsellers Rank: #51,998 in Books (See Top 100 in Books)
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Product Description

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).


From the Publisher

Learning Spark: Lightning-Fast Big Data Analysis Advanced Analytics with Spark: Patterns for Learning from Data at Scale Learning Spark Streaming High Performance Spark: Best practices for scaling and optimizing Apache Spark
Learning Spark Advanced Analytics with Spark Learning Spark Streaming High Performance Spark
Subtitle Lightning-Fast Big Data Analysis Patterns for Learning from Data at Scale Best Practices for Scaling and Optimixing Apache Spark Best practices for scaling and optimizing Apache Spark

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Top Customer Reviews

Format: Kindle Edition Verified Purchase
This book is amazing and it is very good introduction to Apache Spark .. instead of getting lost on the internet.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: HASH(0x9a054138) out of 5 stars 47 reviews
33 of 35 people found the following review helpful
HASH(0x9a09dec4) out of 5 stars Prose is well-written, but style is an impediment to learning. Should be called "Reviewing Spark," not "Learning Spark" April 16 2015
By Silverstein - Published on Amazon.com
Format: Paperback
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
HASH(0x9a09df18) out of 5 stars Start here: Excellent reference for Spark June 16 2015
By Brian Castelli - Published on Amazon.com
Format: Kindle Edition Verified Purchase
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
HASH(0x99f38ec4) out of 5 stars Nice Headstart to Spark Feb. 18 2015
By Sathya Narayanan - Published on Amazon.com
Format: Kindle Edition Verified Purchase
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.
4 of 4 people found the following review helpful
HASH(0xb256ddc8) out of 5 stars Not Comprehensive Dec 18 2015
By Irmak Cakmak - Published on Amazon.com
Format: Kindle Edition
There are notions popped out out-of-the-blue throughout the book. One example is "accumulators". It is first seen in Chapter 3, while describing the aggregate function, yet lacking any definition of it. You have to go and check the programming guide on the official web page. The book is not comprehensive, but it is more like a companion.
3 of 3 people found the following review helpful
HASH(0xa4f8f36c) out of 5 stars Great Overview July 31 2015
By Robert Crowe - Published on Amazon.com
Format: Kindle Edition Verified Purchase
This seems to be the definitive overview of Spark, and fortunately it's well written. It includes basics like setting up a Spark cluster, and topics like streaming and machine learning. I'm glad I read it.


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