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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


Spark provides a unified runtime for big data. Hadoop Distributed File System (HDFS) has traditionally been the most used storage platform for Spark as it has provided the most cost-effective storage for unstructured and semi-structured data on commodity hardware. This has been upended by public cloud storage systems, especially Amazon S3. This edition of the book reflects that reality with special emphasis on connectivity to S3.

That being said, Spark exclusively leverages Hadoop's InputFormat and OutputFormat interfaces. InputFormat is responsible for creating InputSplits from input data and dividing it further into records. OutputFormat is responsible for writing to storage. Following image illustrates InputFormat metaphorically:

We will start by writing to the local filesystem and then move over to loading data from HDFS. In the Loading data from HDFS recipe, we will cover the most common file format: regular text files. We will also explore loading data stored in Amazon S3...

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