The document provides an introduction to NoSQL databases, contrasting them with traditional RDBMS by discussing their limitations and characteristics, such as scalability and flexibility. It outlines the types of NoSQL databases, including key-value, document, column, and graph databases, and addresses their advantages and disadvantages. Additionally, it highlights the CAP theorem and considerations for choosing the appropriate database based on data priorities and query types.

1. Introduction to NoSQL

2. Agenda
RDBMS & its Limitations
ACID v/s BASE
CAP Theorem
Introduction to NOSQL & its Characteristics
Types of NOSQL Databases
Choosing the right fit
Disadvantages
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3. Since 1970
Use SQL to manipulate data
 Easy to use
 Easy to integrate with other system
Fits most of our legacy application demands
Relational DBMS
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4. What is problem of RDBMS?
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5. BASE
Basic Availability: Each request is guaranteed a response—successful or
failed execution
Soft state: The state of the system may change over time, at times without
any input (for eventual consistency)
Eventual consistency: The database may be momentarily inconsistent but will
be consistent eventually

6. You have to choose only two. In almost all cases, you would choose availability over
consistency
CAP Theorem
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7. NoSQL (Not Only SQL) … ??
 A NoSQL database provides a mechanism for storage and retrieval of data that
employs less constrained consistency models than traditional relational databases.
 Motivations : Simplicity of design ; Horizontal scaling ; Availability.
 NoSQL databases are often highly optimized key–value stores intended for simple
retrieval and appending operations, with the goal being significant performance
benefits in terms of latency and throughput.
 Used for : Big Data and real-time web applications.
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8. Why now ??
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9. Characteristic of NoSQL
Large data volumes.
Scalable replication and distribution (Horizontal scaling).
Queries need to return answers quickly.
Asynchronous Inserts & Updates.
Schema-less.
BASE / CAP Theorem.
No Joins statement.
No complicated Relationships
Less administration time(less cost).

10. Types of NoSQL Databases
NoSQL DB family includes several DB types:
Column: HBase, Accumulo, Cassandra
Document: MongoDB, Couchbase
Key-value : Dynamo, Riak, Redis, Cache, Project Voldemort
Graph: Neo4J, Allegro, Virtuoso

11. Data Model: Collection of key/value pairs
Keys and Values can be complex compounds
Designed to handle massive load
No complex query filters
All joins must be in the code
Advantages
 Very fast
 Very scalable
 Simple model
 Able to distribute horizontally
 Very Predictable performance of O(1)
Disadvantages
 Many data structures (objects) can't be easily modeled as key value pairs
Key/Value Databases
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12. Tables are similar to RDBMS, but semi-structured
Based on Google’s BigTable
Rows can have arbitrary columns
Distributed and Decentralized
High Availability & Fault Tolerance
Tunable Consistency
Column Databases
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13. Document Databases
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Inspired by Lotus Notes
Central concept of a Document
Documents encapsulate/encode data in some Encodings:
XML, YAML, JSON, BSON

14. Graph Database
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Based on Graph Theory -> G = (V, E)
Designed for data that is well
represented in a graph
Social networks, public transport links, network
topologies, road maps
Nodes, edges, properties are used to represent and
store data
Graph relationships are query able

15. Which one should I choose ?

16. What’s best depends on your data

17. Priorities
What types of queries do you need to support?
How much data?
Optimized for reads, writes, or updates?
Versioning
How separate is data from app? Will other applications need to access it in
future?

18. And how you want to interact with it
RESTful inteface
Query API
NonSQL query languages
Via indexed values, keys, nodes
File access

19. It too has disadvantages…
Performance and scalability achieved at the expense of feature support
No joins
Grouping and ordering become more problematic
No SQL
No transactions
Eventual consistency v/s Strict consistency
Tools are often lacking

20. Summary
NoSQL :
Handle huge data.
High availability with small cost.
More data redundancy.
High performance.
Less administration time.
Less standards.
SQL :
Good to solve ACID problems.
Expensive.
Less data redundancy.
Increasing availability mean increasing cost.
More standards.
More administration.

21. Thank You !