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Database design

Database design is the process of planning how data is stored, organized, and accessed in a database system. It typically starts with requirements—what information must be captured, how it will be used, and what constraints apply (e.g., uniqueness, validity, security).

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  1. Database design (en-US)

    Database design is the process of planning how data is stored, organized, and accessed in a database system. It typically starts with requirements—what information must be captured, how it will be used, and what constraints apply (e.g., uniqueness, validity, security).

  2. Core steps and key concepts

    Common steps include: (1) conceptual modeling (entities, relationships, and business rules), (2) logical design (tables/collections, attributes, relationships, and normalization), and (3) physical design (indexes, storage layout, partitioning, and performance tuning). Key concepts include primary keys, foreign keys, data types, constraints (NOT NULL, CHECK), normalization to reduce redundancy, and denormalization when justified for performance. Good design also considers scalability, backup/recovery, auditability, and query patterns so the database supports expected workloads efficiently and safely.

  3. FAQ

    • What is normalization? It’s structuring data to minimize redundancy and update anomalies, often using normal forms. • What’s the difference between conceptual and logical design? Conceptual focuses on business concepts; logical translates them into a structured data model. • How do indexes affect performance? Indexes speed up reads for specific queries but add overhead for writes and storage.

FAQ

What is database design?

The planning and structuring of data storage and relationships so the database meets functional requirements and performs reliably.

What are the main phases of database design?

Conceptual modeling, logical design, and physical design (plus ongoing maintenance and optimization).

How do you balance normalization and performance?

Normalize to maintain integrity and reduce duplication, then selectively denormalize or add indexes based on real query and workload needs.

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