Book Image

Practical Big Data Analytics

By : Nataraj Dasgupta
Book Image

Practical Big Data Analytics

By: Nataraj Dasgupta

Overview of this book

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulated in this book.
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface

Enterprise infrastructure solutions


The proper choice of infrastructure also plays a key role in determining the efficiency of the organization's data science platform. Too little, and the algorithms will take too long to execute; too much and you may have a lot of resources remaining unutilized. As such, the latter is preferable to having too little, which thwarts progress and the ability of any machine learning researcher to efficiently perform his or her tasks.

Cloud computing

Over the past 5 - 7 years, organizations have gradually shifted their resources to cloud-based platforms such as Amazon Web Services, Microsoft Azure, and Google Compute Engine. Today, all of these contain extremely sophisticated and extensive architecture to support machine learning, data mining, and in general data science at an enterprise level to meet the needs of organizations of all sizes.

In addition, the concept of images, such as AMI images in Amazon's AWS, allows users to initiate a pre-built snapshot of...