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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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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

Why NoSQL?


The term NoSQL generally means Not Only SQL: that is, the underlying database has properties that are different to those of common and traditional database systems. As such, there is no clear distinction that qualifies a database as NoSQL, other than the fact that they do not provide the characteristics of ACID compliance. As such, it would be helpful to understand the nature of ACID properties that have been the mainstay of database systems for many decades, as well as discuss, in brief, the significance of BASE and CAP, two other terminologies central to databases today.

The ACID, BASE, and CAP properties

Let's first proceed with ACID and SQL.

ACID and SQL

ACID stands for atomicity, consistency, isolation, and durability:

  • Atomicity: This indicates that database transactions either execute in full or do not execute at all. In other words, either all transactions should be committed, that is, persisted in their entirety, or not committed at all. There is no scope for a partial execution...
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