No sqlpresentation

No Sql
Prepared By:
Bouzidi Nayrouz
Gouia Salma
Zarrouk Asma

Introduction1
What is NOSQL?2
Where to use NoSQL?5
Advantages of NOSQL6
Plan
Conclusion7
Need of NoSQL3
NoSQL Types4

A database is a collection of information that is
organized so that it can be easily accessed, managed
and updated. Computer databases typically contain
aggregations of data records or files, containing
information about sales transactions or interactions
with specific customers.
Two main categories : Relational database(SQL) and
NOSQL.
4

Class of non-relational data
storage systems.
Relaxation for one or more of
the ACID properties (Atomicity,
Consistency, Isolation,
Durability) using CAP theorem.
Stands for Not Only SQL
Do not require a fixed table
schema nor do they use the
concept of joins.
6

Just as moving to dynamically-
typed languages (Ruby/Groovy), a
shift to dynamically-typed data with
frequent schema changes
Expansion of Open-
source community. .
NoSQL solution is more
acceptable to a client now
than a year ago.
Explosion of social media sites
(Facebook, Twitter, Google etc.)
with large data needs. (Sharding is
a problem)
Rise of cloud-based solutions
such as Amazon S3 (simple
storage solution).
8

Key Value pair
based
Column based
Document
based
Graph based
10

Key Value pair based
It uses sort of Map
/Dictionary as a
fundamental Data
model
You can’t control data
structure but you can
manipulate value if you
know it’s key
The simplest non-trivial data
model
Data is represented as a
collection of key-value pairs.
11

for storing session information,
user profiles, preferences,
shopping cart data..
We use it
when we need to query data
having relationships between
entities. .
We would avoid it
CouchDB, Oracle NoSQL
Database, Riak etc..
Example
12

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
13

for content management systems,
blogging platforms, log aggregation
We use it
for systems that are in early
development, changing query
patterns.
We would avoid it
Cassandra, HBase, Hypertable,
and Amazon DynamoDB.
Example
14

Document based
The database stores and
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
15

for content management systems,
blogging platforms, web analytics,
real-time analytics, e- commerce
applications.
We use it
for systems that need complex
transactions spanning multiple
operations or queries against
varying aggregate structures.
We would avoid it
Lotus Notes, MongoDB, Couch
DB, Orient DB, Raven DB.
Example
16

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.
17

for connected data, such as social
networks, spatial data, routing
information for goods and supply.
We use it
Neo4J, Infinite Graph, OrientDB,
FlockDB..
Example
18

03
To improve data access performance via some combination of handling
larger data volumes, reducing latency, and improving throughput.
To improve programmer productivity by using a
database that better matches an application's
needs.
NoSQL Data storage systems makes sense for applications that process very large
semi-structured data –like Log Analysis, Social Networking Feeds, Time-based data.
20

Cheap and easy to implement
(open source)
Don't require a schema
Easy to distribute
Data are replicated to multiple nodes and
can be partitioned
What is not
provided by
NoSQL
Joins
Group by
SQL
Integration with
applications
that are based
on SQL
22

All the choices provided by the rise of NoSQL
databases does not mean the demise of RDBMS
databases as Relational databases are a powerful
tool.
We are entering an erea of Polyglot persistence, a
technique that uses different data storage
technologies to handle varying data storage needs. It
can apply across an enterprise or within an individual
application.
24

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