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Learning Spark: Lightning-Fast Big Data Analysis Paperback – 13 February 2015

4.2 out of 5 stars 196 ratings
Edition: 1st

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Frequently bought together

  • Learning Spark: Lightning-Fast Big Data Analysis
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  • High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
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  • Hadoop : The Definitive Guide: Storage and Analysis at Internet Scale
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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 2nd Ed. 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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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).

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Product details

  • Publisher ‏ : ‎ Centre for Alternative Economic Policy Research; 1st edition (13 February 2015)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 274 pages
  • ISBN-10 ‏ : ‎ 1449358624
  • ISBN-13 ‏ : ‎ 978-1449358624
  • Dimensions ‏ : ‎ 17.78 x 1.6 x 23.34 cm
  • Customer Reviews:
    4.2 out of 5 stars 196 ratings

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3.0 out of 5 stars A good foundation, but already outdated
Reviewed in the United Kingdom on 29 January 2018
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3.0 out of 5 stars It's not a bad book, but not sure I can recommend it ...
Reviewed in the United Kingdom on 15 May 2015
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5.0 out of 5 stars Five Stars
Reviewed in the United Kingdom on 14 August 2017
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4.0 out of 5 stars Buen libro para quien quiere comenzar
Reviewed in Spain on 1 November 2017
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Anita Brand
5.0 out of 5 stars Ein Super Buch zum lernen von Spark
Reviewed in Germany on 2 December 2015
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