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

System recommendations


If you're installing Hadoop on a local machine, it is recommended that your system should have at least 4-8 GB of RAM (memory) and sufficient free disk space of at least 50 GB. Ideally, 8 GB or more memory will suffice for most applications. Below this, the performance will be lower but not prevent the user from carrying out the exercises. Please note that these numbers are estimates that are applicable for the exercises outlined in this book. A production environment will naturally have much higher requirements, which will be discussed at a later stage.

Installing analytics software, especially platforms such as Hadoop, can be quite challenging in terms of technical complexity and it is highly common for users to encounter errors that would have to be painstakingly resolved. Users spend more time attempting to resolve errors and fixing installation issues than they ideally should. This sort of additional overhead can easily be alleviated by using virtual machines ...