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Practical Big Data Analytics

You're reading from   Practical Big Data Analytics Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781783554393
Length 412 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
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Toc

Table of Contents (13) Chapters Close

Preface 1. Too Big or Not Too Big FREE CHAPTER 2. Big Data Mining for the Masses 3. The Analytics Toolkit 4. Big Data With Hadoop 5. Big Data Mining with NoSQL 6. Spark for Big Data Analytics 7. An Introduction to Machine Learning Concepts 8. Machine Learning Deep Dive 9. Enterprise Data Science 10. Closing Thoughts on Big Data 11. External Data Science Resources 12. Other Books You May Enjoy

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...

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