data-modeling

Database design from raw business requirements is achieved by defining entities, attributes, and relationships using ERD notation. A phased approach identifies entities, resolves relationship cardinalities, and applies normalization rules to produce consistent schemas.

Many-to-many relationships in data modeling are resolved by introducing associative entities. This approach maps physical database constructs and constraints correctly, ensuring the logical model maintains referential integrity without losing relationship data.

Building a data dictionary involves defining columns with keys, nullability, defaults, and validations. It provides implementation-ready rigor by specifying necessary indexes and constraints to map logical models to physical database types.

Conceptual, logical, and physical data models represent progressive design phases. The workflow moves from identifying high-level entities to defining attributes and resolving relationships, finally mapping to physical database constructs and specific constraints.

Normalization rules are applied during the logical modeling phase of database design. They ensure consistent schemas by organizing attributes to reduce redundancy, preparing the model before mapping to physical database types and constraints.

Generating ERD guidance requires understanding entity types, attribute definitions, and relationship cardinality. The modeling workflow provides a structured path from requirements to physical design, but foundational knowledge of database structures helps interpret the outputs.