Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

Summary


In this chapter, we have covered details around Spark SQL, the DataFrame API, the Dataset API, Catalyst optimiser, the nuances of SparkSession, creating a DataFrame, manipulating a DataFrame, converting a DataFrame to RDD, and vice-versa before providing examples of working with DataFrames. This is by no means a complete reference for SparkSQL and is perhaps just a good starting point for people planning to embark on the journey of Spark via the SQL route. We have looked at how you can use your favorite API without consideration of performance, as Spark will choose an optimum execution plan.

The next chapter is one of my favorite topics - Spark MLLib. Spark provides a rich API for predictive modeling and the use of Spark MLLib is increasing every day. We'll look at the basics of machine learning before providing users with an insight into how the Spark framework provides support for performing predictive analytics. We'll cover topics from building a machine-learning pipeline, feature...