Advantages of NoSQL over SQL

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You must have heard about the NoSQL database and then ‘Non-Relational Database” comes to your mind after it. It stands for “non SQL” or “not only SQL”, which states that it can be used without an SQL language but not a direct replacement of relational database.

What is NoSQL?

A NoSQL Database provides flexible schema and stores data differently other than relational databases. This means that you can store and retrieve data that is not in a tabular form. The idea behind that is to overcome the scalability issue in relational databases, promote the simplicity of design, and add the feature of horizontal scaling to the database. There are four major types of NoSQL databases that are evolved over the period i.e. document databases, graph databases, wide-column stores, and key-value databases.

What is SQL?

A SQL stands for Structured Query Language. It is a collection of structured tables and a table is a collection of records or rows that are also called as an attribute of a table. SQL programming language is used with relational database management systems to perform crud operations on the database as needed and match the data with the predefined characteristics. Many renowned databases support SQL engine such as MS SQL Server, MS Access, and Oracle. It also support open source databases like Maria DB, MySQL, and PostgreSQL.

Advantages of NoSQL Database

You can see the advantages of NoSQL over SQL database here:

Support for Unstructured, Semi-Structured, and Structured Data

The most important advantage of NoSQL database is the support of unstructured database. Relational databases store data in structured format that has a predefined schema and a proper data model design must be placed before the data starts getting in the database. Once the data is loaded into the database, you can retrieve the stored data from it with the help of SQL programming language and send it to the connected application. The application will send back the data from the user to the database.

NoSQL database has flexible schema and can be controlled easily by the developers. It is easy to adapt the new formats in schema. It can also handle the indexes in unstructured data by using a native feature or an integrated set of services. Many companies are now able to make better decisions by gaining insights of their businesses with the help of unstructured data.

It support the following data formats:

  • Big Data

It supports Big Data that contains large and complex data set from existing or new sources. The volume of such data sources is so big that it is unable to manage with the traditional relational database management system.

  • JSON Files

It supports JSON files that is a light weighted interchange format. It consists of nested data with key-pair values. It can handle the very large and complex nested structure of JSON file efficiently without affecting the performance of an application.

  • Graph Database

It can help you in traversing relationships between properties of multiple nodes.

  • Binary Data

It has the provision of supporting binary values, maps, lists etc.

  • Columnar Data

It provides you the support of saving data into column rather than the rows.

Horizontal Scalability

NoSQL database allows you to handle database across serval servers. For example, you can add more servers that are inexpensive to your main database cluster if the data size is growing more than the expected size. This addition is called as scaling database at a horizontal level. On the other hand, relational database requires more powerful and eventually more expensive servers to do the scaling at vertical level.

The addition of inexpensive addition of hardware including server and storage makes the cluster more durable and resilient in terms of infrastructure. In NoSQL, the hardware can be upgraded and new clusters can be added into the existing environment with zero downtime. This type of architecture always allows the set of clusters of a database to expand and contract automatically.

New Technology

NoSQL is a recent development in a database field; therefore, it does not need to support legacy hardware and applications.  New major and minor features are released frequently in NoSQL database whereas on the other hand, SQL databases has major releases after every three to five years.

Developer Friendly

NoSQL allows the developer to create the data model closes to the real world application such as data objects used in an application to send or receive data from the database. It stores the data in native format, which means that the developer can adapt the format of an application easily.

Programming Language

SQL is the most common used language in relational database management systems to perform database operations on it. In NoSQL, you can also have the functionality of writing SQL queries such as in Business Intelligence (BI) tools by using its own access language to interpret sending and receiving of data within the database. It helps the developers to focus on what is required in the application instead of designing data model first and going into the technicalities of the database structure.

Ideal Use Case Application

NoSQL database is considered as a better choice for unstructured data formats like documents or JSON. Whereas, on the other hand, SQL databases are used to handle simultaneous transactions in multiple rows of a table.

NoSQL Database Systems

In NoSQL databases, MongoDB is considered as the most popular NoSQL database, by the developers. It has the following features and benefits:

  • No cost of hardware
  • Dynamic schema
  • It can be extended horizontally (Horizontally Scalable)
  • Exceptional performance for performing simple queries
  • Addition and deletion of new columns and fields without creating an impact on existing rows and ongoing application

Making the decision

Although there are many advantages of NoSQL over SQL, but picking the right database for your organization is an important decision. NoSQL databases are gaining popularity and are proving to be a game changer in the industry. Many companies have started Big Data and Hadoop to support large and complex data queries.

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