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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

Understanding the evolution of schema awareness

Spark can process data from various data sources, such as HDFS, Cassandra, and relational databases. Big data frameworks (unlike relational database systems) do not enforce a schema while writing data into it. HDFS is a perfect example of where any arbitrary file is welcome during the write phase. The same is true with Amazon S3. Reading data is a different story, however. You need to give some structure to even completely unstructured data to make sense out of it. With this structured data, SQL comes in very handy, when it comes to making sense out of some data.

Getting ready

Spark SQL is a component of the Spark ecosystem, introduced in Spark 1.0 for the first time. It incorporates a project named Shark...

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