Book Image

Apache Spark Quick Start Guide

By : Shrey Mehrotra, Akash Grade
Book Image

Apache Spark Quick Start Guide

By: Shrey Mehrotra, Akash Grade

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.

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Table of Contents (10 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to Apache Spark
Introduction to Apache Spark
What is Spark?
Spark architecture overview
Spark language APIs
Spark components
Making the most of Hadoop and Spark
Summary
2
Apache Spark Installation
Apache Spark Installation
AWS elastic compute cloud (EC2)
Configuring Spark
Summary
3
Spark RDD
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
4
Spark DataFrame and Dataset
Spark DataFrame and Dataset
DataFrames
Datasets
Summary
5
Spark Architecture and Application Execution Flow
Spark Architecture and Application Execution Flow
A sample application
Application execution modes
Application monitoring
Summary
6
Spark SQL
Spark SQL
Spark SQL
Summary
7
Spark Streaming, Machine Learning, and Graph Analysis
Spark Streaming, Machine Learning, and Graph Analysis
Spark Streaming
Machine learning
Graph processing
Summary
8
Spark Optimizations
Spark Optimizations
Cluster-level optimizations
Application optimizations
Summary
9
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