This book offers a step-by-step approach to learning Hunk, diving into the technical aspects of it first. It will demonstrate the various aspects of big data analytics using the powerful capabilities of Hunk. In addition to this, it provides detailed sections on the deployment and configuration of Hunk on top of Hadoop and the NoSQL data stores. It will also teach you how to create queries using SPL, reports, and dashboards. This book covers security questions and demonstrates how to set up security for big data implementation based on Hadoop and Hunk. Moreover, it will teach you how to use the Hunk SDK and extend its default functionality. Finally, it acts as a guide to deploying Hunk on top of MongoDB and AWS Elastic MapReduce.
Chapter 1, Meet Hunk, covers Hunk and its basic features. Hunk is a full-featured platform to rapidly explore, analyze, and visualize data in Hadoop and the NoSQL data stores. You will learn how to install and configure Hunk. Moreover, you will learn about Hunk's architecture and Hunk Virtual Index. You will also be introduced to loading data into Hadoop in order to aid its discovery by Hunk.
Chapter 2, Explore Hadoop Data with Hunk, talks about how you can easily analyze and visualize data using the Splunk search processing language (SPL). Getting a large amount of data into Hadoop is easy but getting analytics from this data is the challenge. You will learn about the use cases of big data analytics and the security aspect of Hunk.
Chapter 3, Meet Hunk Features, teaches you about Hunk's knowledge objects. Hunk is a powerful big data analytics platform, which gives us many tools in order to explore, analyze, and visualize big data. You will learn how to build a semantic layer on top of Hadoop and discover data using the friendly user interface of Hunk Pivot.
Chapter 4, Adding Speed to Reports, covers the techniques related to report acceleration. Hunk is an extremely powerful tool and can handle a vast amount of data. However, business decisions, which depend on fresh data, can't wait.
Chapter 5, Customizing Hunk, introduces REST API, SDK, and so on. Sometimes, we want to get out of the box or need to meet business expectations and are restricted by the initial functionality. Thus, you will learn how to create customized visualization, and you will also be introduced to the Splunk Web Framework.
Chapter 6, Discovering Hunk Integration Apps, introduces you to Hunk's apps that can easily integrate with the NoSQL data stores, such as MongoDB or Sqqrl. Hunk is a universal big data analytics platform, which can easy explore data in Hadoop or the NoSQL data stores. You will learn how to connect MongoDB and explore data in its data store.
Chapter 7, Exploring Data in the Cloud, shows you how to analyze data in AWS Cloud. Some big organizations prefer to store their big data on the cloud because it gives them many benefits.
In this book, you will learn how to explore, analyze, and visualize big data in Hadoop or the NoSQL data stores with the powerful, full-featured big data analytics platform, Hunk. You will discover real-world examples, dive into Hunk's architecture and capabilities, as well as learn how to build Operation Intelligence using this technology. Additionally, you will learn about report acceleration techniques, data models, and custom dashboards and views using Hunk. Moreover, this book focuses on popular use cases using powerful Hunk apps, which provide integration with the NoSQL data stores and give complete visibility into your end-to-end big data operations. Finally, you will about the Splunk web framework. We just require a laptop or PC with a 4 GB RAM (8 GB RAM recommended) and VirtualBox installed. There aren't any specific hardware requirements as VirtualBox should work everywhere.
If you are big data enthusiast and want to get more business insight and build efficient, real-time Operation Intelligence Solution based on Hadoop deployments or various NoSQL data stores using Hunk, this book is for you. Aimed on big data developers, managers and consultants this is also a comprehensive reference for everyone, who want to learn how to analyze and explore big data with one of the most powerful and flexible big data analytics platform.
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: "Rename the count field as qty."
A block of code is set as follows:
<property>
<name>dfs.permissions.enabled</name>
<value>true</value>
</property>
<property>
<name>dfs.permissions</name>
<value>true</value>
</property>
<property>
<name>hadoop.proxyuser.root.hosts</name>
<value>*</value>
</property>
<property>
<name>hadoop.proxyuser.root.groups</name>
<value>*</value>
</property>Any command-line input or output is written as follows:
[cloudera@quickstart ~]$ whoami cloudera [cloudera@quickstart ~]$ sudo su [root@quickstart cloudera]# whoami root [root@quickstart cloudera]#
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: "Go to menu Machine | Add and point to the extracted VBOX file."
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You can download the example code files from http://www.bigdatapath.com/wp-content/uploads/2015/05/learning-hunk-05-with-mongo.zip.
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