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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

Introduction


Before we start this chapter, it is important that we discuss some trends that directly affect how we develop applications. 

Big data applications can be divided into the following three categories:

  • Batch
  • Interactive
  • Streaming or continuous applications

When Hadoop was designed, the primary focus was to provide cost-effective storage for large amounts of data. This remained the main show until it was upended by S3 and other cheaper and more reliable cloud storage alternatives. Compute on this large amounts of data in the Hadoop environment was primarily in the form of MapReduce jobs. Since Spark took the ball from Hadoop (OK! Snatched!) and started running with it, Spark also reflected batch orientation focus in the initial phase, but it did a better job than Hadoop in the case of exploiting in-memory storage. 

Note

The most compelling factor of the success of Hadoop was that the cost of storage was hundreds of times lower than traditional data warehouse technologies, such as Teradata...

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