The volume of data that one has to deal has exploded to unimaginable levels in the past
decade, and at the same time, the price of data storage has systematically reduced.
Private companies and research institutions capture terabytes of data about their users’
interactions, business, social media, and also sensors from devices such as mobile phones
and automobiles. The challenge of this era is to make sense of this sea of data. This is
where big data analytics comes into picture.
Big Data Analytics largely involves collecting data from different sources, munge it in a
way that it becomes available to be consumed by analysts and finally deliver data products
useful to the organization business.
The process of converting large amounts of unstructured raw data, retrieved from different
sources to a data product useful for organizations forms the core of Big Data Analytics.
In this tutorial, we will discuss the most fundamental concepts and methods of Big Data Analytics.
This tutorial has been prepared for software professionals aspiring to learn the basics of Big Data Analytics. Professionals who are into analytics in general may as well use this tutorial to good effect.
Before you start proceeding with this tutorial, we assume that you have prior exposure to
handling huge volumes of unprocessed data at an organizational level.