MySQL vs MongoDB

Requires a predefined schema, where the structure of the database and tables must be declared before data insertion

Lacks a rigid schema, providing the flexibility to adapt to changing data requirements without requiring alterations to the database schema.

Uses SQL (Structured Query Language) for queries, which is powerful for relational data.

Utilizes a rich query language that resembles JavaScript. Queries are expressed as JSON-like documents, making it intuitive for developers familiar with Javascipt.

Ideal for applications with well-defined and stable schemas, such as traditional relational databases for business applications.

Suited for projects where flexibility, scalability, and real-time analytics are crucial, such as content management systems, IoT applications, and log/event tracking.

Strong emphasis on ACID properties with robust transaction support.

Sacrifices strict ACID properties for performance, supporting atomic operations within a single document.

Mature and widely adopted, known for reliability.

Gaining popularity rapidly, offers simplicity in data modelling and agility.

Well-established and widely used, MySQL has extensive community support and a wealth of resources. It is known for its reliability and maturity.

While gaining popularity rapidly, MongoDB, being a NoSQL database, might be perceived as less mature than MySQL.

Organizes data in traditional tables, rows, and columns, following a relational model.

Utilizes a document-oriented approach, storing data in BSON documents, which allows for more natural representation of data structures.

Relies on SQL queries for aggregating and manipulating data.

Features a powerful aggregation framework that enables complex transformations, filtering, and analysis of data within the database.