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 (12 chapters)

Appendix . Theres More with Spark

We've covered some of the hottest areas in Spark from the new Catalyst optimizer to RDDs and DataFrames. We have covered the MLLib and GraphX library before looking at some use cases to see how an application can be built on Spark. However, as this book is just an introduction, we have skipped various important topics along the way. This was intentional as we wanted to keep the book at a readable level to help you get started, but with pointed references along the way that can help you master a particular topic. However, there are certain key areas which we would like to cover as a part of an Appendix, which we believe are important for you to develop and deploy your Spark applications.

In this Appendix, we would like to cover

  • Performance tuning Spark
    • Data serialization
    • Memory management
    • Sizing up your executors
    • Handling skew
  • Security
  • Key configuration properties
  • Configuring Jupyter with Spark
  • Shared variables: advanced

Let's get started.