Fast Data Processing with Spark

By Holden Karau
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  1. Installing Spark and Setting Up Your Cluster

About this book

Spark is a framework for writing fast, distributed programs. Spark solves similar problems as Hadoop MapReduce does but with a fast in-memory approach and a clean functional style API. With its ability to integrate with Hadoop and inbuilt tools for interactive query analysis (Shark), large-scale graph processing and analysis (Bagel), and real-time analysis (Spark Streaming), it can be interactively used to quickly process and query big data sets.

Fast Data Processing with Spark covers how to write distributed map reduce style programs with Spark. The book will guide you through every step required to write effective distributed programs from setting up your cluster and interactively exploring the API, to deploying your job to the cluster, and tuning it for your purposes.

Fast Data Processing with Spark covers everything from setting up your Spark cluster in a variety of situations (stand-alone, EC2, and so on), to how to use the interactive shell to write distributed code interactively. From there, we move on to cover how to write and deploy distributed jobs in Java, Scala, and Python.

We then examine how to use the interactive shell to quickly prototype distributed programs and explore the Spark API. We also look at how to use Hive with Spark to use a SQL-like query syntax with Shark, as well as manipulating resilient distributed datasets (RDDs).

Publication date:
October 2013
Publisher
Packt
Pages
120
ISBN
9781782167068

About the Author

  • Holden Karau

    Holden Karau is a software development engineer and is active in the open source. She has worked on a variety of search, classification, and distributed systems problems at IBM, Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor's of mathematics degree in computer science. Other than software, she enjoys playing with fire and hula hoops, and welding.

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