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

Analyzing Nobel Laureates data with MongoDB


In the first exercise, we will use MongoDB, one of the leading document-oriented databases, to analyze Nobel Laureates from 1902-present. MongoDB provides a simple and intuitive interface to work with JSON files. As discussed earlier, JSON is a flexible format that allows representing data using a structured approach.

JSON format

Consider the following table:

Firstname

Lastname

Information

John

15

Subject: History, Grade B

Jack

18

Subject: Physics, Grade A

Jill

17

Subject: Physics, Grade A+

 

The Information field contains a column containing multiple values categorized under Subject and Grade. Such columns that contain multiple data are also known as columns with nested data.

Portability has been an important aspect of transferring data from one system to another. In general, ODBC connectors are used to transfer data between database systems. Another common format is CSV files with the data represented as comma-separated values. CSV files are optimal for structured...