Introduction

NoSQL databases differ from traditional, relational databases by not requiring a uniform data structure. Instead, NoSQL databases are designed with more flexibility, making them more adaptable to managing unstructured data, such as social media posts or multimedia content. Due to their high scalability and low cost compared to traditional relational databases, many companies and applications are starting to adapt and use NoSQL databases for their big data storage needs.

Types of NoSQL Databases

Document-oriented Databases

One of the most popular varieties of NoSQL databases are document-oriented databases. They utilize JSON or BSON formatted documents to store data as a group of objects with key-value pairs. Popular examples include Apache CouchDB and MongoDB.

Key-Value Databases

Key-Value databases, such as Apache Cassandra and Riak, are great for partition tolerance, easy scalability, and low read/write processing per second performance demands. Because of their structure, Key-Value databases are suitable for handling simple operations with small content.

Graph Databases

Graph databases are used to store data that interact extensively together. Items in these databases consist of vertices or nodes, and relationships of data existing between these points. Examples of graph databases include Neo4j and OrientDB.

Column-family Database

Column-family databases are similar to data warehouses rather than standard databases. Data in column-family databases can consist of any variety of data, ranging from images to CSV files. Apache HBase, Cassandra, Composite-indexed Vector Provided Objects, and Solid have evolved into carrying more usage as a high-performance engine designed for large scale-out commitments and batch data processing requirements.

When to Use NoSQL Databases

NoSQL databases can be used in many scenarios where traditional relational databases might not possess the agility, size, scalability, or performance to easily store large quantities of varied data.,Thus, it is an excellent data storage option for organizations requiring significant amounts of collected summary data from either legacy systems or applications. High-speed applications that require dynamic queries or frequent updates represent another place where a transition to NoSQL can make the database achieving enterprise-level performances. Many Big Data initiatives also include experienced NoSQL technologies coupled with Hadoop.

Examples of NoSQL Databases

MongoDB

MongoDB retains a valuable storehouse of unstructured data. The BSON format aligned to JavaScript notation and indexing against it facilitates matching queries thereby providing dynamic data formation and upkeep operations, including flexible handling of aggregation methodologied via a selective map-reduce model

Apache Cassandra

Apache Cassandra provided the utmost severe scale and transmits data continuously at very high throughout rates with creating heat paths if errors occur or issues arose with scaling the setup.

Redis

Redis invites backing and retention of high-speed websites, and harmonious library frameworks mostly Active state with regularly repeated transmission capabilities highly useful that will always get RAM used as data store backup aides efficiently.

Neo4j

Neo4j is one of the persistent Graph Database Structures that permit handling of individual interactions by arranging output points in assorted details by way of inquiring Big Data.