✨ MongoDB - Introduction

What is MongoDB?

MongoDB is a NoSQL database known for its flexibility, scalability, and speed. It was created by Dwight Merriman, Eliot Horowitz, and Kevin Ryan in 2007 while they were developing a cloud computing platform at 10gen (now MongoDB Inc.). The name "MongoDB" is derived from "humongous," highlighting its capability to manage massive volumes of data. Released in 2009, MongoDB quickly gained popularity and became open-source, enabling developers around the world to contribute to its growth.

Unlike traditional relational databases, MongoDB stores data in a document-oriented format using JSON-like BSON (Binary JSON) documents. This structure allows for high adaptability to both unstructured and semi-structured data. It is widely used in applications that require dynamic data models and horizontal scalability.

Relational vs. Non-Relational Databases

1. Relational Databases (RDBMS)

• Definition: Use a structured approach, organizing data into tables with rows and columns. Each row represents a record, and each column represents a field.
• Structure: Fixed schema; structure must be defined before data insertion.
• Data Integrity: Enforced through constraints like primary keys and foreign keys.
• Examples: MySQL, PostgreSQL, SQL Server, Oracle.

2. Non-Relational Databases (NoSQL)

• Definition: Use flexible data models like document-based, key-value, wide-column, and graph databases.
• Structure: Schema-less or dynamic schema, allowing for changes without affecting the entire database.
• Scalability: Typically scale horizontally by adding more servers (nodes) to distribute data.
• Examples: MongoDB (document), Redis (key-value), Cassandra (wide-column), Neo4j (graph).

Choosing MongoDB vs. MySQL

MongoDB is ideal for applications that require:

• High scalability and performance
• Flexible, evolving data structures
• Real-time analytics and big data processing

Common use cases include social media platforms, IoT applications, and content management systems.

MySQL is suitable for applications that demand:

• Structured data with defined relationships
• Strong data integrity and consistency (ACID compliance)
• Complex queries and transactions

Typical use cases include banking systems, ERP platforms, and enterprise business applications.