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Learning spark: lightning-fast big data analysis

By: Contributor(s): Material type: TextTextPublication details: O'Reilly Media 2015 BeijingDescription: xvi, 276 pISBN:
  • 9789351109945
Subject(s): DDC classification:
  • 006.312 K2L3
Summary: 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 https://www.oreilly.com/library/view/learning-spark/9781449359034/
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Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 4-A / Slot 108 (0 Floor, West Wing) Non-fiction General Stacks 006.312 K2L3 (Browse shelf(Opens below)) Available 202153

Table of contents

Introduction to data analysis with Spark
Downloading Spark and getting started
Programming with RDDs
Working with key/​value pairs
Loading and saving your data
Advanced Spark programming
Running on a cluster
Tuning and debugging Spark
Spark SQL
Spark streaming

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

https://www.oreilly.com/library/view/learning-spark/9781449359034/

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