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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Preface

The success of Hadoop as a big data platform raised user expectations, both in terms of solving different analytics challenges and reducing latency. Various tools evolved over time, but when Apache Spark came, it provided a single runtime to address all these challenges. It eliminated the need to combine multiple tools with their own challenges and learning curves. Using memory for persistent storage besides compute, Apache Spark eliminates the need to store intermediate data on disk and increases processing speed up to 100 times. It also provides a single runtime, which addresses various analytics needs, such as machine-learning and real-time streaming, using various libraries.
This book covers the installation and configuration of Apache Spark and building solutions using Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries.


For more information on this book's recipes, please visit infoobjects.com/spark-cookbook.
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