Book Image

Practical Real-time Data Processing and Analytics

Book Image

Practical Real-time Data Processing and Analytics

Overview of this book

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 possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own. We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Chapter 12. Working with Apache Flink

In this chapter, we get the readers acquainted with Apache Flink as a candidate for real-time processing. While most of the appendages in terms of data source tailing and sink remain the same, the compute methodology is changed to Flink and the integrations and topology wiring are very different for this technology. Here the reader will understand and implement end-to-end Flink processes to parse, transform, and converge compute on real-time streaming data.

In this chapter, we will look at the following topics:

  • Flink architecture and execution engine
  • Flink basic components and processes
  • Integration of source stream to Flink
  • Flink processing and computation
  • Flink persistence
  • FlinkCEP
  • Gelly
  • Examples and DIY
  • Source to sink: Flink execution
  • Executing storm topology on Flink