Book Image

Learning Hunk

By : Dmitry Anoshin, Sergey Sheypak
Book Image

Learning Hunk

By: Dmitry Anoshin, Sergey Sheypak

Overview of this book

Hunk is the big data analytics platform that lets you rapidly explore, analyse, and visualize data in Hadoop and NoSQL data stores. It provides a single, fluid user experience, designed to show you insights from your big data without the need for specialized skills, fixed schemas, or months of development. Hunk goes beyond typical data analysis methods and gives you the power to rapidly detect patterns and find anomalies across petabytes of raw data. This book focuses on exploring, analysing, and visualizing big data in Hadoop and NoSQL data stores with this powerful full-featured big data analytics platform. You will begin by learning the Hunk architecture and Hunk Virtual Index before moving on to how to easily analyze and visualize data using Splunk Search Language (SPL). Next you will meet Hunk Apps which can easy integrate with NoSQL data stores such as MongoDB or Sqqrl. You will also discover Hunk knowledge objects, build a semantic layer on top of Hadoop, and explore data using the friendly user-interface of Hunk Pivot. You will connect MongoDB and explore data in the data store. Finally, you will go through report acceleration techniques and analyze data in the AWS Cloud.
Table of Contents (14 chapters)

Big data analytics


We are living in the century of information technology. There are a lot of electronic devices around us that generate lots of data. For example, you can surf the Internet, visit a couple of news portals, order new Nike Air Max shoes from a web store, write a couple of messages to your friends, and chat on Facebook. Every action produces data. And we can multiply the actions by the amount of people who have access to the Internet, or just use a mobile phone, and we get really big data. Of course, you have a question: how big is big data? It probably starts from terabytes or even petabytes now. The volume is not the only issue; we are also struggling with the variety of data. As a result, it is not enough to analyze just the data structure. We should explore unstructured data, such as machine data generated by various machines.

World-famous enterprises try to collect this extremely big data in order to monetize it and find business insights. Big data offers us new opportunities; for example, we can enrich customer data through social networks, using the APIs of Facebook or Twitter. We can build customer profiles and try to predict customer wishes in order to sell our product or improve the customer experience. It is easy to say, but difficult to do. However, organizations try to overcome these challenges and use big data stores, such as Hadoop.