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Fast Data Processing with Spark - Second Edition

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Perform real-time analytics using Spark in a fast, distributed, and scalable way

About This Book

  • Develop a machine learning system with Spark's MLlib and scalable algorithms
  • Deploy Spark jobs to various clusters such as Mesos, EC2, Chef, YARN, EMR, and so on
  • This is a step-by-step tutorial that unleashes the power of Spark and its latest features

Who This Book Is For

Fast Data Processing with Spark - Second Edition is for software developers who want to learn how to write distributed programs with Spark. It will help developers who have had problems that were too big to be dealt with on a single computer. No previous experience with distributed programming is necessary. This book assumes knowledge of either Java, Scala, or Python.

What You Will Learn

  • Install and set up Spark on your cluster
  • Prototype distributed applications with Spark's interactive shell
  • Learn different ways to interact with Spark's distributed representation of data (RDDs)
  • Query Spark with a SQL-like query syntax
  • Effectively test your distributed software
  • Recognize how Spark works with big data
  • Implement machine learning systems with highly scalable algorithms

In Detail

Spark is a framework used 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 built-in tools for interactive query analysis (Spark SQL), large-scale graph processing and analysis (GraphX), and real-time analysis (Spark Streaming), it can be interactively used to quickly process and query big datasets.

Fast Data Processing with Spark - Second Edition covers how to write distributed 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 developing analytics applications and tuning them for your purposes.

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