TY  - BOOK
AU  - Acharya, Seema 
TI  - Demystifying NoSQL
SN  - 9788126579969
U1  - 005.75 
PY  - 2020///
CY  - New Delhi
PB  - Wiley India Pvt. Ltd.
KW  - Non-relational databases
KW  - SQL (Computer program language)
N1  - Table of content
Preface

About the Author

Acknowledgements

Chapter 1 Getting Started with NoSQL

1.1 What has Changed in the Last Decade?

1.2 History of NoSQL

1.3 What is NoSQL?

1.4 Why NoSQL?

1.5 NoSQL Databases

1.6 Types of NoSQL Databases

1.7 SQL versus NoSQL

1.8 ACID versus BASE

1.9 CAP Theorem

Chapter 2 Types of NoSQL Databases

2.1 Introduction

2.2 Key−Value Pair Databases

2.3 Document Databases

2.4 Column-Family Databases

2.5 Graph Database

Chapter 3 Column-Family Store

3.1 Introduction to Apache Cassandra

3.2 Features of Cassandra

3.3 Cassandra Query Language Data Types

3.4 Cassandra Query Language Shell (Cqlsh)

3.5 Collections

3.6 Cassandra Counter Column

3.7 Time-to-Live (TTL)

3.8 Alter Commands

3.9 Import from and Export to CSV

3.10 Querying System Tables

 

Chapter 4 MongoDB

4.1 What is MongoDB?

4.2 Why MongoDB?

4.3 Terms used in RDBMS and MongoDB

4.4 CRUD Operations

Chapter 5 Neo4j: A Graph-Based Database

5.1 Introduction to Graph Database

5.2 Creating Nodes

5.3 Create a Relationship

5.4 WHERE Clause

5.5 Creating a Complete Path

5.6 Create Index

5.7 Create Constraints

5.8 Select Data with MATCH

5.9 Fetch All Nodes

5.10 Drop an Index

5.11 Drop a Constraint

5.12 Delete a Node

5.13 Delete Multiple Nodes

5.14 Delete All Nodes

5.15 Delete a Relationship

5.16 Merge Command

Chapter 6 NoSQL Database Orientation

6.1 RDBMS or NoSQL?

6.2 Key–Value Store

6.3 Column Family Store

6.4 Document Store

6.5 Graph Store

6.6 Examples of NoSQL Databases

Annexure A – Project 1 in MongoDB Database

Annexure B – Project 2 in MongoDB Database

Annexure C – Possible Interview Questions and Answers

Index


N2  - NoSQL databases are non-relational, open-source, distributed, schema-less, and cluster friendly databases. They are hugely popular today owing to their ability to scale out or scale horizontally and the adeptness at dealing with a rich variety of data: structured, semi-structured and unstructured data. They are malleable and flexible enough to accommodate sparse datasets, besides maintaining cost efficiency and availability
ER  -