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Apache Spark 2.x Cookbook

You're reading from  Apache Spark 2.x Cookbook

Product type Book
Published in May 2017
Publisher
ISBN-13 9781787127265
Pages 294 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Apache Spark 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

Chapter 3. Spark SQL

Spark SQL is a Spark module for processing structured data. It had a humble start, but now it has become the most important Spark library (as DataFrames/Datasets are replacing RDDs).

This chapter is divided into the following recipes:

  • Understanding the evolution of schema awareness
  • Understanding the Catalyst optimizer
  • Inferring schema using case classes
  • Programmatically specifying the schema
  • Understanding the Parquet format
  • Loading and saving data using the JSON format
  • Loading and saving data from relational databases
  • Loading and saving data from an arbitrary source
  • Understanding joins
  • Analyzing nested structures

We will start with a small journey down memory lane to see how schema awareness has slowly evolved into a Spark framework and has now become the core of it. After this, we will discuss how the Catalyst optimizer, the core engine of Spark, works. In the next two recipes, we will focus on converting data from raw format into DataFrames. Then we will discuss how to seamlessly...

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