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SQL vs NoSQL: High-Level Differences SQL databases are primarily called as Relational Databases (RDBMS); whereas NoSQL database are primarily called as non-relational or distributed database. SQL databases are table based databases whereas NoSQL databases are document based, key-value pairs, graph databases or wide-column stores. This means that SQL databases represent data in form of tables which consists of n number of rows of data whereas NoSQL databases are the collection of key-value pair, documents, graph databases or wide-column stores which do not have standard schema definitions which it needs to adhered to. SQL databases have predefined schema whereas NoSQL databases have dynamic schema for unstructured data. SQL databases are vertically scalable whereas the NoSQL databases are horizontally scalable. SQL databases are scaled by increasing the horse-power of the hardware. NoSQL databases are scaled by increasing the databases servers in the pool of resources to reduce the load. SQL databases uses SQL ( structured query language ) for defining and manipulating the data, which is very powerful. In NoSQL database, queries are focused on collection of documents. Sometimes it is also called as UnQL (Unstructured Query Language). The syntax of using UnQL varies from database to database. SQL database examples: MySql, Oracle, Sqlite, Postgres and MS-SQL. NoSQL database examples: MongoDB, BigTable, Redis, RavenDb, Cassandra, Hbase, Neo4j and CouchDb

Replication: By replicating MySQL database across multiple nodes the work load can be reduced heavily increasing the scalability and availability of business application

Sharding: MySQL sharding os useful when there is large no of write operations in a high traffic website. By sharding MySQL servers, the application is partitioned into multiple servers dividing the database into small chunks. As low cost servers can be deployed for this purpose, this is cost effective.

Memcached as a NoSQL API to MySQL: Memcached can be used to increase the performance of the data retrieval operations giving an advantage of NoSQL api to MySQL server.

Maturity: This database has been around for a long time and tremendous community input and testing has gone into this database making it very stable. Wide range of Platforms and Languages: MySql is available for all major platforms like Linux, Windows, Mac, BSD and Solaris. It also has connectors to languages like Node.js, Ruby, C#, C++, C, Java, Perl, PHP and Python. Cost effectiveness: It is open source and free.

Denormalization Denormalization can be defined as the copying of the same data into multiple documents or tables in order to simplify/optimize query processing or to fit the user’s data into a particular data model. Most techniques described in this article leverage denormalization in one or another form.

Aggregates All major genres of NoSQL provide soft schema capabilities in one way or another:

Key-Value Stores and Graph Databases typically do not place constraints on values, so values can be comprised of arbitrary format. It is also possible to vary a number of records for one business entity by using composite keys. For example, a user account can be modeled as a set of entries with composite keys like UserID_name, UserID_email, UserID_messages and so on. If a user has no email or messages then a corresponding entry is not recorded. BigTable models support soft schema via a variable set of columns within a column family and a variable number of versions for one cell. Document databases are inherently schema-less, although some of them allow one to validate incoming data using a user-defined schema.