This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.

1. Introduction to NOSQL database

2. Content
• History of database technology
• Motivation for Nosql databases
• Benefits of Relational databases
• Limitations of RDBMS
• What is Nosql
• CAP Theorm
• Types of Nosql
• Research Challanges
• Conclusion
• References

3. Summary of early database systems
Database
name
Advantage Limitations
Flat file systems • Keep data about a single entity
together in a single record.
• Can leads to duplicated data and
inefficient retrieval.
• Difficult to implement security controls
to protect confidential data
Hierarchical data
management
systems
• Follows parent-child
relationships
• Stores records in the form of
records.
searching for a customer in a loan
database might require scanning all
customer records.
Network data
management
• The ability to represent parent-
child and many-to-many
relations is an advantage.
• Duplicate data,
• Difficulty implementing security,
Inefficient searching
• Difficulty maintaining program
• code to access databases
Relational Database
Management
Systems
• Store in the form of table.
• Normalization
• ACID property
• SQL
• Storage management programs
• Memory management programs
• Data dictionary
• Query language

4. Motivations for No SQL Databases
• Big data supports storing and querying huge
amounts of semi-structured and unstructured data.
• e.g Facebook and google stores and process
exabyte and zettabyte of data.
• So we need databases which can provide:
Scalability(Sharding)
 Cost
 Flexibility
 Availability

5. Source:http://programming4.us/enterprise/18762.aspx

6. Benefits of Relational databases
• Based on ACID
properties
• Strong consistency,
concurrency, recovery
• Normalization
• Standard Query
language (SQL)
• Vertical scaling (up
scaling)

7. Limitations of RDBMS
 Relational databases
were not built for
distributed applications.
Because:
• Joins are expensive
• Hard to scale
horizontally
• Expensive (product cost,
hardware)
 The rise of big data
(volume,Variety)
https://www.slideshare.net/ramakantsoni/p
resentation-on-no-sql

8. What is NoSQL
• It stands for 'NOT ONLY SQL'
• #NoSQL was a twitter hashtag for a conference in 2009
used by ERIC EVANS.
• There is no strict defination for NoSQL databases.
• It is a nonrelational database.
• Mainly designed to use for Big Data and Real time web
applications.

9. Advantages of Nosql over RDBMS
• Can handle Semi-structured
and unstructured data.
• Data Models- No Predefined
Schema.
• Scaling- Scaling out/
Horizontal Scaling.
• Avoids overhead of ACID
transactions.
• Avoids complexity of SQL
query.
Source:https://deavid.wordpress.com/2018
/08/29/nosql-databases

10. CAP Theorem
• Consistency: Clients should read the
same data.
• Availability: Data to be available all
time.
• Partial Tolerance: Data to be
partitioned across network segments
due to network failures.
Source:https://www.researchgate.net/figure/CAP-
theorem

11. NoSQL Types
It can be classified into
four types:
Key Value pair based
Column based
Document based
Graph based
Source:https://deavid.wordpress.com/2018/08/29/nosql-databases

12. Key Value Pair Based
• Data model: (key, value) pairs.
• Designed for processing dictionary.
• Dictionaries contain a collection of
records having fields containing data.
• Records are stored and retrieved
using a key that uniquely identifies
the record,and is used to quickly find
the data with in the database.
• Example: Oracle NoSQL Database,
Riak etc.
• We use it for: storing session
information, user profiles,
preferences , shopping cart data. Key Value Pair
Based[1].

13. Column based
• It store data as Column families
containing rows that have many
columns associated with a row
key.Each row can have different
columns.
• Column families are groups of
related data that is accessed
together.
• Example:Cassandra, HBase,
Hypertable, and Amazon
DynamoDB.
• We use it for content
management systems ,blogging
platforms, log aggregation.
Column based [1]

14. Document Based
• The database store send retrieves
documents. It stores documents in
the value part of the key-value
store.
• Self describing, hierarchical tree
data structures consisting of
maps, collections, and scalar
values.
• Example: Lotus Notes ,Mongo
DB, Couch DB, Orient DB, Raven
DB.
• We use it for content management
systems, blogging platforms,
webanalytics,real-timeanalytics e-
commerce applications.
Document Based [1]

15. Graph Based
• Store entities and relationships
between these entities as nodes
and edges of a graph respectively.
Entities have properties.
• Traversing the relationships is
very fast as relationship between
nodes is not calculated at query
time but is actually persisted as a
relationship.
• Example: Neo4J, InfiniteGraph,
OrientDB, FlockDB.
• It is well suited for connected data
such as social networks ,spatial
data ,routing information for goods
and supply.
Graph Based[1]

16. Research Challenges
• Transaction Processing : Nosql do not strictly follow ACID
properties.
• Query Processing: There is no user friendly unified query
language for Nosql.
• Security: Since follow unstructured data approach along
with geographic distribution. Hence its very difficult to
apply security.

17. Conclusion
• RDBMS is a great tool for
solving ACID problems
• When data validity is super
important
• When you need to support
dynamic queries
• NoSQL is a great tool for
solving data availability
problems
• When it’s more important
to have fast data than right
data
• When you need to scale
based on changing
requirements

18. References
1. Ali Davoudian and Liu Chen, Mengchi Liu:A Survey on NoSQL
Stores,ACM Comput. Surv. 51, 2, Article,40 (April 2018),43 pages.
2. Dan Sullivan:Nosql for mere Mortals,1st Edition,United States of
America:Pearson Education,2015.
3. Xiangdong Huang, Jianmin Wang, Yu Zhong, Shaoxu Song, and
Philip S. Yu. 2015:Optimizing data partition for scaling out NoSQL
cluster. Concurrency and Computation: Practice and Experience 27,
18, 5793–5809.
4. Katarina Grolinger, Wilson A HigashinoEmail author, Abhinav Tiwari
and Miriam AM Capretz:Data management in cloud environments:
NoSQL and NewSQL data stores,l. Journal of Cloud Computing:
Advances, Systems and Applications,Springer. 2013

19. Thank You