Chapter 1, Setting Up Environment, serves as a step-by-step guide to set up your environment, including Java, Hadoop, Elasticsearch, and useful plugins. Test the environment setup by running a WordCount job to import the results to Elasticsearch.

Chapter 2, Getting Started With ES-Hadoop, walks you through how the WordCount job was developed. We will take a look at the real-world problem to solve it better by introducing Elasticsearch to the Hadoop ecosystem.

Chapter 3, Understanding Elasticsearch, provides you detailed understanding on how to use Elasticsearch for full text search and analytics. With practical examples, you will learn indexing, search, and aggregation APIs.

Chapter 4, Visualizing Big Data Using Kibana, introduces Kibana with real-world examples to show how you can give different shapes and colors to your big data. It demonstrates how to discover your data and visualize it in dynamic dashboards.

Chapter 5, Real-Time Analytics, shows how Elasticsearch and Apache Storm can solve your real-time analytics requirements by classifying and streaming tweets data. We will see how to harness Elasticsearch to solve the data mining problem of anomaly detection.

Chapter 6, ES-Hadoop In Production, explains how Elasticsearch and the ES-Hadoop library works in distributed deployments and how to make the best out of it for your needs. It provides you practical skills to tweak configurations for your specific deployment needs. We will have a configuration checklist for production and skills to administer the cluster.

Chapter 7, Integrating with the Hadoop Ecosystem, takes practical examples to show how you can integrate Elasticsearch with Hadoop ecosystem technologies, such as Pig, Hive, Cascading, and Apache Spark.

Appendix, Configurations, contains a list of configuration options for any of the Hadoop ecosystem integrations.

A major part of the book is focused on practical examples. You can get the maximum out of this book by actually trying out the examples in this book. Throughout the book, you will have specific instructions on how to set up the software and tools required whenever it is needed.

A Linux-based physical or virtualized machine is all you need to for this book. All instructions and examples of the book are tested for the Ubuntu 14.04 distribution. However, these examples should run fine on other Linux flavors and operating systems as well with relevant changes in OS-specific commands. For Windows users, the easiest way would be to set up Ubuntu VM with virtualization tools (such as VirtualBox or VMware).

This book is targeted at Java developers who have a basic knowledge of Hadoop. No prior Elasticsearch experience is expected.

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, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the include directive."

A block of code is set as follows:

{
  "properties": {
    "skills": {
      "type": "string",
      "analyzer": "simple"
    }
  }
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

        StatusListener listener = new StatusListener() {
        public void onStatus(Status status) {
                queue.offer(status);
            }
        … 
    …

Any command-line input or output is written as follows:

SELECT c.city, s.skill, avg(c.experience)

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: "Clicking the Next button moves you to the next screen."

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