What is NOSQL and its Skills in Big data?

The Big Data field is getting more popular day by day. A giant pile of data that is either structured or unstructured yet compiled together. To deal with this data, we use a lot of technologies and skills. To handle databases, one of the most commonly used skills is SQL. It stands for Structured Query Language. But what about unstructured data? There is now a new skill that works for both structured and unstructured data. It is termed NoSQL. NoSQL is an abbreviated form of Not Only SQL. So, What actually is NoSQL? How does it work? In this article, you will get all your answers related to NoSQL.

What is NoSQL and How is it different from SQL?

 Earlier the data was collected with a pretty straightforward approach. The data used to follow certain columns and was limited to that approach only.  SQL follows static schema and supports a few predefined operations. The introduction of big data brought all the possible data. Data can be structured, unstructured, or even truncated. In big data, the data no longer follows any predefined format. Thus, NoSQL is defined for this purpose. NoSQL supports everything that SQL does and also supports unstructured data too. There is no need to follow any format or static schema in NoSQL. The databases in NoSQL are very flexible and are suitable for NoSQL.

Features of NoSQL in Big Data

Highly flexible and scalable

The architecture of NoSQL makes it easy to install many servers and machines in the architecture. Thus making it more scalable. Also, NoSQL does not follow and traditional approach to holding data thus we can import any kind of data to it, making it flexible.

Fast Development

 NoSQL supports great user experience and supports fast queries. This is all because of the data models and faster iterations. The models are highly adaptable to the environment.

Horizontal Scaling

SQL supports vertical scaling. It refers to working with a single system or cluster and increasing its processing power. However, this vertical scaling has its limit. So, Horizontal scaling is used in NoSQL. In horizontal scaling, the system is divided into many nodes to share the processing load. Thus, increasing the processing power as much as the user wishes.

Efficient Data Management

In NoSQL, the data is stored in the form of documents. It is not stored in relational databases. It supports the storage to store data in any format. That is why for big data NoSQL is preferred.

Types of NoSQL Databases

NoSQL databases are further divided into 4 parts namely:

  • Key Value
  • Graph-Based
  • Document Based
  • Column Stores

Key Value

It is one of the simplest and easiest forms of a NoSQL database. The data is stored in the form of a Key and value pair. For example,


S.no here is a key and Qty is a value. This signifies that S.no 1 has a quantity of 12. You can access the value using the key and vice-e-versa is also true. The data type can be a string, integer, decimal, object, or any other user-defined data type. There are only 2 columns needed for this kind of database. It is very fast, scalable, and easy to use. Some of the key value-based databases are Riak, Couchbase, Redis, etc.


Graph-based databases work with the nodes. The data is stored in the form of nodes and each node is linked to make meaningful data. The primary focus of a graph-based database is on the relationships among the nodes. The speed of a query depends on the number of nodes and their relationships with one another. More the nodes more time it will take. However, accessing data becomes easy once the relationship is known between the nodes. OrientDB and Noe4J are examples of graph-based databases.

This is an example of a graph-based database. All these circles represent nodes and they are linked together


If you wish to store data in the form of documents then you use document-based databases. XML, JSON, and BSON are the formats supported to store documents. A document can also be referred to as a record or a row in such databases.  Accessing elements can be easy as fast using indexes in document-based. There is a limitation of atomicity in it. This means we cannot update two different documents together.  One of the most famous and used document-based databases is MongoDB.

Column Stores

In the column stores data is stored in the form of columns. These are non-relational databases. Performing aggregate operations in such types of databases is easier as compared to the other ones. Retrieving data becomes very easy and fast in column-based databases. Examples of such types of databases are DynamoDB, Hypertable, etc.

Job Opportunities Requiring NoSQL Skills

NoSQL is in very much demand. This increase in demand leads to growth in job opportunities too. Some of the career opportunities in NoSQL are:

1. Database Administrator

The role of a database administrator is to store and organize data. Before big data organizing the data was easy.  After big data organizing has become a little difficult. To become a database administrator one is required to have knowledge of many databases.

2. Application/Software Developer

NoSQL databases are widely used in applications and software. Although many apps store the data in a particular format yet there is a scope to have an unstructured entry. The most commonly used database in application development is MongoDB.

3. Data Scientist

Being a data scientist requires a lot of experience and skills with data. Data scientists collect the data, analyze it and present the data in a human-understandable format. The average salary of a data scientist is 10 Lakhs per anum in India.

4. Database Architect

The profession of an architect requires a lot of experience and knowledge of NoSQL databases. Migration of data, building data models, and analyzing are some of the tasks performed by them.

Also, Read!!

How to Become a Big Data Engineer


NoSQL databases are very popular nowadays. Learning one will make you realize how to handle giant unstructured data. The use of NoSQL databases is becoming very common with big data. Having a NoSQL skill will help you excel in big data. There is a wide range of possible opportunities after acquiring this skill.

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