Feedback from our readers is always welcome. Let us know what you think about this book-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of. To send us general feedback, simply email [email protected], and mention the book's title in the subject of your message. If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.
-
Book Overview & Buying
-
Table Of Contents
Practical Real-time Data Processing and Analytics
By :
Practical Real-time Data Processing and Analytics
By:
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 (14 chapters)
Preface
Free Chapter
Introducing Real-Time Analytics
Real Time Applications – The Basic Ingredients
Understanding and Tailing Data Streams
Setting up the Infrastructure for Storm
Configuring Apache Spark and Flink
Integrating Storm with a Data Source
From Storm to Sink
Storm Trident
Working with Spark
Working with Spark Operations
Spark Streaming
Working with Apache Flink
