Learning Spark: Lightning-Fast Big Data Analysis

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"O'Reilly Media, Inc.", 28.01.2015. - 276 страница
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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
 

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Ауторска права

Друга издања - Прикажи све

Learning Spark: Lightning-Fast Big Data Analysis
Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia
Ограничен приказ - 2015
Learning Spark: Lightning-Fast Big Data Analytics
Mark Hamstra,Holden Karau,Matei Zaharia,Andy Konwinski,Patrick Wendell
Приказ није доступан - 2015

Чести термини и фразе

accumulators Aggregations algorithms Amazon S3 Apache Hive AvgCount batch Benefit from Partitioning cache Cassandra chapter Collaborative Filtering Components of Execution compute configuration count create data scientists DataFrames datasets default driver program elements executors fault tolerance filesystem filter function Hadoop Hadoop input HDFS HiveContext import Integer JDBC JSON launch level of parallelism Linear regression Loading CSV Logistic regression machine learning MapReduce Maven memory MLlib mode multiple nullable NumPy object files options Output Operations package PageRank pair RDDs parameters partitioner Per­Partition pipeline Python Python and Scala query random forests reduceByKey regression result Saving Scala Shells Scala val scheduling script SequenceFiles serialization shown in Example spam Spark Application Built Spark SQL Spark Streaming spark-submit SparkConf SparkContext Standalone cluster manager Stateless Transformations storage String tasks text files tweets UDFs vectors worker nodes

О аутору (2015)

Holden Karau is a software development engineer at Databricks and is active in open source. She is the author of an earlier Spark book. Prior to Databricks she worked on a variety of search and classification problems at Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelors of Mathematics in Computer Science. Outside of software she enjoys playing with fire, welding, and hula hooping.

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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QR code for Learning Spark
НасловLearning Spark: Lightning-Fast Big Data Analysis
АуториHolden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia
Издавач"O'Reilly Media, Inc.", 2015
ISBN1449359051, 9781449359058
Дужина276 страница
  
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