NoSQL

Types of NoSQL Database and examples of NoSQL database.

What is NoSQL Database?

NoSQL Database is also known as  ” Not only SQL “, is a non-tabular database system, unlike a relational database. NoSQL provides a flexible, scalable, and high-performance database to the users. They are majorly used for real-time web applications and big data.

Traditional RDBMS uses SQL syntax for storage and retrieval of data, while NoSQL is ideally used for storing structured, semi-structured, unstructured, and polymorphic data.

When working with a massive amount of data, the response time for RDBMS is slow while NoSQL can be used for a large amount of data.

Types of NoSQL Database

The four major types of NoSQL Databases are Key-value, Document-based, Wide-column store, and Graph Database.

1) Key-Value Database:

KeyValue

 

A key-value is a simpler non-relational database that stores data in key-value pair,  where the key serves as a unique identifier. The key contains the name, while the value contains the content.

The key value has no query language and they provide an efficient way of storage, retrieval, and updation of data using the simple get, put, and delete commands. These are the least complicated NoSQL databases.

Key-value Database example:

  • Oracle NoSQL Database
  • Redis
  • Voldemort
  • Aerospike

Advantages of Key-value Database:

  • Due to its simplicity, the key-value pair is more preferable and it is easy to read and write operations.
  • Key-value stores are flexible schemas and can store values including JSON data.

Disadvantages of Key-value Database:

  • Optimized only for data with a single key and value; for storing multiple values a parser is required.
  • Not optimized for the lookup table.

2) Document Based Database:

Documentdb
Image Credit: VectorStock
A Document database is a non-relational type of database, designed for storing data in JSON like documents with data type descriptions and values. They are great for content management platforms such as blogs and video platforms.
Document databases organize data into groups called collections, which are equivalent to tables in a relational database.

Document Database example:

  • MongoDB
  • Apache CouchDB
  • OrientDB
  • CouchBase Server

Advantages of Document Database:

  • Document database has no defined schema, and every document can be stored differently.
  • These are easily scalable and flexible.
  • They handle a large volume of data at high speed.

Disadvantages of Document Database:

  • The main disadvantage is that it lacks security.
  • These are less reliable than relational databases.

3) Wide Column Database:

Wide Column stores data in rows, tables, and dynamic columns. Also considered as two-dimensional key-value databases. The names and format of columns can vary from row to row in the same table.

Well suited for analyzing a large set of data, and Cassandra is well known for it. It can handle ambiguous and complex datatypes including unformatted texts.

Wide Column Store example:

  • Amazon DynamoDB
  • Cassandra
  • Apache HBase

Advantages of Wide Column Store:

  • Since data is stored column-wise rather than a row, it is faster and more efficient than other database systems.
  • It can store a high volume of data.
  • Column databases are used in data warehouses.

Disadvantages of Wide Column Store:

  • Online Transactions are not supported in the columnar database.
  • Not suitable for incremental data loading.
  • Single row retrieval can be slow.

4) Graph-Based Database:

GrpahDB
Image Credit: TechCrunch.com

A Graph Database is a database system that uses graph structures for semantic queries with edges, nodes, and properties to store data.

A Non-Relational Database system with CRUD(Create, Read, Update, and Delete) Operations working on a Graph model. It allows you to include different kinds of objects and relationships in the graph. The usage of Graph Database has grown over the past few years.

Graph Database example:

  • Neo4j
  • OrientDB
  • FlockDB
  • ArangoDB

Advantages of Graph Database:

  • Better problem-solving.
  • Update data in real-time and support queries simultaneously.
  • Graph Database serves as a great AI infrastructure, due to well structured relational information between the entities.

Disadvantages of Graph Database:

  • No uniform query language.
  • Not efficient in processing a high volume of data.

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