Learning Real Time processing with Spark Streaming

Building scalable and fault-tolerant streaming applications made easy with Spark streaming

About This Book

• Process live data streams more efficiently with better fault recovery using Spark Streaming
• Implement and deploy real-time log file analysis
• Learn about integration with Advance Spark Libraries – GraphX, Spark SQL, and MLib.

Who This Book Is For

This book is intended for big data developers with basic knowledge of Scala but no knowledge of Spark. It will help you grasp the basics of developing real-time applications with Spark and understand efficient programming of core elements and applications.

What You Will Learn

• Install and configure Spark and Spark Streaming to execute applications
• Explore the architecture and components of Spark and Spark Streaming to use it as a base for other libraries
• Process distributed log files in real-time to load data from distributed sources
• Apply transformations on streaming data to use its functions
• Integrate Apache Spark with the various advance libraries like MLib and GraphX
• Apply production deployment scenarios to deploy your application

In Detail

Using practical examples with easy-to-follow steps, this book will teach you how to build real-time applications with Spark Streaming.

Starting with installing and setting the required environment, you will write and execute your first program for Spark Streaming. This will be followed by exploring the architecture and components of Spark Streaming along with an overview of libraries/functions exposed by Spark. Next you will be taught about various client APIs for coding in Spark by using the use-case of distributed log file processing. You will then apply various functions to transform and enrich streaming data. Next you will learn how to cache and persist datasets. Moving on you will integrate Apache Spark with various other libraries/components of Spark like Mlib, GraphX, and Spark SQL. Finally, you will learn about deploying your application and cover the different scenarios ranging from standalone mode to distributed mode using Mesos, Yarn, and private data centers or on cloud infrastructure.

Style and approach

A Step-by-Step approach to learn Spark Streaming in a structured manner, with detailed explanation of basic and advance features in an easy-to-follow Style. Each topic is explained sequentially and supported with real world examples and executable code snippets that appeal to the needs of readers with the wide range of experiences.

Table of Contents

Chapter 1. Installing and Configuring Spark and Spark Streaming
Chapter 2. Architecture and Components of Spark and Spark Streaming
Chapter 3. Processing Distributed Log Files in Real Time
Chapter 4. Applying Transformations to Streaming Data
Chapter 5. Persisting Log Analysis Data
Chapter 6. Integration with Advanced Spark Libraries
Chapter 7. Deploying in Production