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Apache Spark Quick Start Guide

By : Shrey Mehrotra, Grade

3 (1)

Buy this Book

Apache Spark Quick Start Guide

3 (1)

By: Shrey Mehrotra, Grade

Buy this Book

Overview of this book

Apache Spark is a ?exible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Apache Spark

What is Spark?

Spark architecture overview

Spark language APIs

Spark components

Making the most of Hadoop and Spark

Summary

Apache Spark Installation

AWS elastic compute cloud (EC2)

Configuring Spark

Summary

Spark RDD

What is an RDD?

Programming using RDDs

Transformations and actions

Types of RDDs

Caching and checkpointing

Understanding partitions

Drawbacks of using RDDs

Summary

Spark DataFrame and Dataset

DataFrames

Datasets

Summary

Spark Architecture and Application Execution Flow

A sample application

Application execution modes

Application monitoring

Summary

Spark SQL

Summary

Spark Streaming, Machine Learning, and Graph Analysis

Spark Streaming

Machine learning

Graph processing

Summary

Spark Optimizations

Cluster-level optimizations

Application optimizations

Summary

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Introduction to Apache Spark

Apache Spark is an open source framework for processing large datasets stored in heterogeneous data stores in an efficient and fast way. Sophisticated analytical algorithms can be easily executed on these large datasets. Spark can execute a distributed program 100 times faster than MapReduce. As Spark is one of the fast-growing projects in the open source community, it provides a large number of libraries to its users.

We shall cover the following topics in this chapter:

A brief introduction to Spark
Spark architecture and the different languages that can be used for coding Spark applications
Spark components and how these components can be used together to solve a variety of use cases
A comparison between Spark and Hadoop