Apache Spark 2 for Beginners

Develop large-scale distributed data processing applications using Spark 2 in Scala and PythonAbout This Book This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2 Perform efficient data processing, machine learning and graph processing using various Spark components A practical guide aimed at beginners to get them up and running with SparkWho This Book Is For If you are an application developer, data scientist, or big data solutions architect who is interested in combining the data processing power of Spark from R, and consolidating data processing, stream processing, machine learning, and graph processing into one unified and highly interoperable framework with a uniform API using Scala or Python, this book is for you. What You Will Learn Get to know the fundamentals of Spark 2 and the Spark programming model using Scala and Python Know how to use Spark SQL and DataFrames using Scala and Python Get an introduction to Spark programming using R Perform Spark data processing, charting, and plotting using Python Get acquainted with Spark stream processing using Scala and Python Be introduced to machine learning using Spark MLlib Get started with graph processing using the Spark GraphX Bring together all that you've learned and develop a complete Spark applicationIn Detail Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists. This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application. By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark. Style and approach Learn about Spark's infrastructure with this practical tutorial. With the help of real-world use cases on the main features of Spark we offer an easy introduction to the framework.

Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Spark Fundamentals; An overview of Apache Hadoop; Understanding Apache Spark; Installing Spark on your machines; Python installation; R installation; Spark installation; Development tool installation; Optional software installation; IPython; RStudio; Apache Zeppelin; References; Summary; Chapter 2: Spark Programming Model; Functional programming with Spark; Understanding Spark RDD; Spark RDD is immutable; Spark RDD is distributable; Spark RDD lives in memory.

Spark RDD is strongly typedData transformations and actions with RDDs; Monitoring with Spark; The basics of programming with Spark; MapReduce; Joins; More actions; Creating RDDs from files; Understanding the Spark library stack; Reference; Summary; Chapter 3: Spark SQL; Understanding the structure of data; Why Spark SQL?; Anatomy of Spark SQL; DataFrame programming; Programming with SQL; Programming with DataFrame API; Understanding Aggregations in Spark SQL; Understanding multi-datasource joining with SparkSQL; Introducing datasets; Understanding Data Catalogs; References; Summary.

Chapter 4: Spark Prog…