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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

Inferring schema using case classes


In schema-aware formats, such as Parquet and JSON. This is far from the reality, though. A lot of the time data comes in raw format. The next two recipes will cover how to attach a schema to raw data. 

In an ideal world, data is stored in schema-aware formats, such as Parquet and JSON. This is far from the reality, though. A lot of the time, data comes in raw format. The next two recipes will cover how to attach a schema to raw data. Case classes are special classes in Scala that provide you with the boilerplate implementation of the constructor, getters (accessors), equals, and hashCode to implement Serializable. Case classes work really well to encapsulate data as objects. Readers familiar with Java, can relate it to plain old Java objects (POJOs) or Java beans.

The beauty of case classes is that all that grunt work, which is required in Java, can be done with the case classes in a single line of code. Spark uses the reflection feature of the Java programming...

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