Normalization vs Non-Relational Database

Developers should learn normalization when designing or maintaining relational databases to prevent data duplication, ensure accuracy, and facilitate easier querying and updates meets developers should learn and use non-relational databases when dealing with big data, real-time applications, or scenarios requiring horizontal scalability and flexible schemas. Here's our take.

Normalization

Nice Pick

Developers should learn normalization when designing or maintaining relational databases to prevent data duplication, ensure accuracy, and facilitate easier querying and updates

Pros

• It is crucial in applications with complex data relationships, such as enterprise systems, e-commerce platforms, or any scenario requiring reliable data management, as it minimizes the risk of inconsistencies and optimizes storage
• Related to: relational-database, sql

Cons

• Specific tradeoffs depend on your use case

Non-Relational Database

Developers should learn and use non-relational databases when dealing with big data, real-time applications, or scenarios requiring horizontal scalability and flexible schemas

Pros

• They are ideal for use cases like social media feeds, IoT data streams, content management systems, and recommendation engines where data relationships are complex or evolving
• Related to: mongodb, cassandra

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Normalization is a concept while Non-Relational Database is a database. We picked Normalization based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Normalization wins

Based on overall popularity. Normalization is more widely used, but Non-Relational Database excels in its own space.