This book will have basic to advanced recipes on real-time computing. We will cover technologies such as Flink, Spark and Storm. The book includes practical recipes to help you to process unbounded streams of data, thus doing for real-time processing what Hadoop did for batch processing. You will begin with setting up the development environment and proceed to implement stream processing. This will be followed by recipes on real-time problems using Rabbit-MQ, Kafka, and Nifi along with Storm, Spark, Flink, Beam, and more. By the end of this book, you will have gained a thorough understanding of the fundamentals of NRT and its applications, and be able to identify and apply those fundamentals to any suitable problem.
This book is written in a cookbook style, with plenty of practical recipes, well-explained code examples, and relevant screenshots and diagrams.
Section – A: Introduction – Getting Familiar
This section gives the readers basic familiarity with the real-time analytics spectra and domains. We talk about the basic components and their building blocks. This sections consist of the following chapters:
Section – B: Setup and Infrastructure
This section is predominantly setup-oriented, where we have the basic components set up. This sections consist of the following chapters:
Section – C: Storm Computations
This section predominantly focuses on exploring Storm, its compute capabilities, and its various features. This sections consist of the following chapters:
Section – D: Using Spark Compute In Real Time
This section predominantly focuses on exploring Spark, its compute capabilities, and its various features. This sections consist of the following chapters:
- Chapter 9: Working with Spark
- Chapter 10: Working with Spark Operations
- Chapter 11: Spark Streaming
Section – E: Flink for Real-Time Analytics This section focuses on exploring Flink, its compute capabilities, and its various features.
- Chapter 12: Working with Apache Flink
Section – F: Let’s Have It Stringed Together
This sections consist of the following chapters:
- Chapter 13: Case Study 1
The book is intended to graduate our readers into real-time streaming technologies. We expect the readers to have fundamental knowledge of Java and Scala. In terms of setup, we expect readers to have basic maven, Java, and Eclipse set up to run the examples.
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 will be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, path names, dummy URLs, user input, and Twitter handles are shown as follows: "Once the kafka_2.11-0.10.1.1.tgz file is downloaded, extract the files."
A block of code is set as follows:
cp kafka_2.11-0.10.1.1.tgz /home/ubuntu/demo/kafkacd /home/ubuntu/demo/kafkatar -xvf kafka_2.11-0.10.1.1.tgz
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."
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