Practical Real-time Data Processing and Analytics

A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenario

About This Book

• Learn about the various challenges in real-time data processing and use the right tools to overcome them
• This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems
• A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time

Who This Book Is For

If you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great.

What You Will Learn

• Get an introduction to the established real-time stack
• Understand the key integration of all the components
• Get a thorough understanding of the basic building blocks for real-time solution designing
• Garnish the search and visualization aspects for your real-time solution
• Get conceptually and practically acquainted with real-time analytics
• Be well equipped to apply the knowledge and create your own solutions

In Detail

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as

Table of Contents

Chapter 1. Introducing Real-Time Analytics
Chapter 2. Real Time Applications – The Basic Ingredients
Chapter 3. Understanding And Tailing Data Streams
Chapter 4. Setting Up The Infrastructure For Storm
Chapter 5. Configuring Apache Spark And Flink
Chapter 6. Integrating Storm With A Data Source
Chapter 7. From Storm To Sink
Chapter 8. Storm Trident
Chapter 9. Working With Spark
Chapter 10. Working With Spark Operations
Chapter 11. Spark Streaming
Chapter 12. Working With Apache Flink
Chapter 13. Case Study