Learning Spark: Lightning-Fast Big Data Analysis

Learning Spark: Lightning-Fast Big Data Analysis
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450 грн
37828
ISBN
978-1449358624
Издательство
O'Reilly Media
Оригинальное название
Learning Spark: Lightning-Fast Big Data Analysis
Категория
Базы данных
Номер издания
5-е изд.
Год
2015
Страниц
276
Формат
70х100 1/16 (170х240 мм)
Обложка 
Твердая
Тип бумаги 
Офсет
Язык
Английский
Иллюстрации
Чёрно-белые
Вес, г
615
1 человек
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Подробнее о доставке

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

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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