Spark 2.0 for Beginners

Develop large-scale distributed data processing applications using Spark 2.0 in Scala and Python

About This Book

• This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2.0
• It is aimed at beginners to get them up and running with Spark
• This book will help you explore the subject and develop your knowledge quickly and pain-free

Who 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 2.0 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 with Spark using Scala and Python
• Get started with with graph processing with Spark using Scala
• Develop a complete Spark application

In 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 which that are equally useful for application developers as well as data scientists. SparkR or “R on Spark” in the Spark framework opened the door of Spark data processing capability to the R users.

This book starts with the fundamentals of Spark 2.0 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 the 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.

Table of Contents

Chapter 1: Spark Fundamentals
Chapter 2: Spark Programming Model
Chapter 3: Spark SQL
Chapter 4: Spark Programming with R
Chapter 5: Spark Data Analysis with Python
Chapter 6: Spark Stream Processing
Chapter 7: Spark Machine Learning
Chapter 8: Spark Graph Processing
Chapter 9: Designing Spark Applications