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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 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 (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Introduction

Spark provides a unified runtime for big data. HDFS, which is Hadoop's filesystem, is the most used storage platform for Spark as it provides cost-effective storage for unstructured and semi-structured data on commodity hardware. Spark is not limited to HDFS and can work with any Hadoop-supported storage.

Hadoop supported storage means a storage format that can work with 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.

We will start with 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. In the next recipe, we will cover how to use any InputFormat interface to load data in Spark. We will also explore loading data stored in Amazon S3, a leading cloud storage platform.

We will explore loading...

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