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Introduction: applied for unstructured and schema less data. NoSQL

Introduction:

NoSQL (not only SQL) is a
non-relational database management system. It is used for fast information
retrieval database and is portable. NoSQL can be applied for unstructured and schema
less data. NoSQL databases are open source, appropriated in nature and in
addition it is having high performance directly that is horizontally scalable. Non-relational
database does not have a schema and sort out its data in related tables (i.e.,
data is stored in a non-normalized way). Distributed means information is
spread to various machines and is overseen by various machines so here it
utilizes the idea of information replication.

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CAP Theorem:

Eric Brewer was the philosophical fuel
behind the NoSQL databases. CAP Stands for Consistency, Availability and
Partition Tolerance. The theorem claims that “in a distributed system, when there is an inevitable
network partition (and the cluster breaks into two or more “islands”), you
can’t guarantee both availability (for updates) and consistency.”. According to this any distributed
system cannot guaranty C, A and P simultaneously

·     
Consistency: All the nodes in the distributed
system sees the same data. The system is said to be consistent if we start a
transaction (read or write) in a consistent state and ends with the system in a
consistent state. In this model if a system steps into an inconsistent state during
a transaction then the system gets rolled back in consistent state if there is
an in error in transaction. Examples are SQL, MYSQL and PostgreSQL.

·     
Availability: In a distributed system, if the
system is 100% operational all the time then we have achieved availability. Every
client gets the response regardless of his individual state of the node in the
system. Examples are SQL, MYSQL and PostgreSQL. So, we can say that the
relational databases come under the CA category. Document oriented databases
like Elastic search also fall under this CA.

·     
Partition
Tolerance: If
a system is partition tolerant then we can say that it can sustain any amount
of network failure that does not result in the entire network failure. Data is
replicated across the combinations of nodes and network to keep the system out
of network failures. Examples the storage systems that come under the umbrella
of CP are Redis and MongoDB. The storage systems that come under the AP
umbrella are Cassandra, CouchDB and dynamoDB.    

 

Types of NOSQL
Databases:

·     
Key-Value
Databases: It
is the simplest of all the types of NoSQL. In this the data is stored in the
form of key-value pair. Stored values can be of any type like JSON, string,
text document and so on which can be accessed by a key. Each value has a unique
key this is a drawback for key-value databases for generating a unique key for
every value. When we look back to CAP Theorem these databases fall under
Availability and partition but lack of Consistency. Examples Redis, Riak, and
BerkeleyDB.

·     
Document
Store Databases:
In this the data is stored in the form of documents. These are semi-structured
data stores. The data is stored in the form of key value pairs similar to key
value data stores but the only difference is the values stored has some
structural encoding like BSON (Binary encoding of JSON), JSON (JavaScript
Object Notation), XML. Data can be retrieved. Examples are Couchbase and
MongoDB.

·     
Column
Store Databases:
In this the data is stored in the form of column rather than rows.  Column oriented databases are those in which
the values containing columns are put together into column families. These can
query large data set tables faster. Examples are Cassandra, HBase, Google Big Table.

·     
Graph
Databases: In
this we define a graphical representation of data. The data is stored in nodes
and the edges are used to connect the nodes. Because of its graphical
representation of the data, it supports richer representations of data
relationships. Nodes and relationships both have some define properties. The
graph has nodes which have defined properties and these nodes have some relationships
which is shown by the directional edges. Examples are IBMGraph, Neo4j and Titan.

SQL
vs NoSQL:

·     
Speed: SQL requires higher degree of normalization
i.e. the data is broken down into small relational tables to avoid data
redundancy and duplication of data. It helps manage data in an efficient way
but having several tables reduces the performance of data processing. In NoSQL
data is stored horizontally where the data is duplicated repeatedly and hardly
we ever partition the data but it is stored in the form of entity. So, read and
write operations through a single entity is easier and faster.

·     
For
DB types: SQL
databases can be open source or closed depending upon the commercial vendors.

In NoSQL databases can be classified on the way of storing data as key-value
store, document store, column store or graph store databases

·     
Data
Recovery: when
there is a crisis NoSQL databases can easily recover the data, as NoSQL
databases are unstructured and data is stored in the form of documents.

Conclusion:

            Due to tremendous rise in use of the
internet Google and Facebook faced real time problems while handling a huge
amount of data. We are entering a time of bilingual persistence, a method that
utilizations distinctive data storage technologies to deal with changing data
storage needs. Bilingual persistence can apply over an enterprise or a single
page application.

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