Structured Database

A structured database is a system that organizes data into predefined schemas, typically using rows and columns (as in relational databases). This structured approach enforces consistent data types and relationships, enabling efficient querying, analysis, and management of the stored information.

Also known as: Relational database, tabular database

Comparisons

• Structured Database vs. Unstructured Database: In a structured database, data follows a fixed schema (e.g., tables with rows and columns), while unstructured databases store data without a predefined format.

• Structured Database vs. NoSQL: NoSQL databases often support more flexible data models (key-value, document, graph) compared to the rigid schema of structured databases.

• Structured Database vs. Data Warehouse: While both store structured data, a data warehouse often aggregates information from multiple sources for large-scale analytical queries.

Pros

• Predictable schema: Simplifies data validation and ensures consistent data types.

• Powerful querying: Standardized query languages (e.g., SQL) enable complex joins and aggregations.

• Data integrity: Constraints and relationships help maintain consistent, accurate records.

Cons

• Rigid design: Schema changes can be time-consuming if data requirements evolve.

• Scalability challenges: Horizontal scaling and sharding can be more complex than in some NoSQL solutions.

Example

A sales platform stores customer, order, and product data in a structured database (tables), linking them through primary and foreign keys. This design facilitates quick retrieval of order histories and product details and ensures consistent data across the system.