What are the most common SQL data modeling techniques and when should you use them?

Krill Technology employs several SQL data modeling techniques, including: Star Schema: Ideal for data warehousing, it organizes data into facts surrounded by dimensions, enhancing query performance. Snowflake Schema: Efficient for normalized data, it extends star schema by further normalizing dimensions. Third Normal Form (3NF): Ensures data integrity by minimizing redundancy through normalization, suitable for transactional databases. Dimensional Modeling: Balances query performance and data integrity by structuring data into fact and dimension tables, commonly used in data warehousing. Entity-Relationship Diagrams (ERD): Provides a visual representation of data entities and relationships, aiding in database design and understanding

Conceptual modeling is great because it does not depend on the future structure of the data and the technology (relational DB or other) with which this data will be created. This stage allows you to understand the main business idea or concept, what data, how and for what is used in the information system. And you should always remember that you need to build two models “As Is” and “As To Be”.

Yes, Conceptual Data Model can incorporate key constraints and data types to bridge the gap between high-level business understanding and technical implementation, offering a more comprehensive view of the system's architecture and aiding in smoother transition to logical and physical models.

I do not fully agree that logical data model is a more detailed and refined version of the conceptual data model. Logical modeling is a different look at the nature of the data used in the domain. At this step, the model must answer the question of which data structure (data processing logic) is most suitable for the concept being used independent of specific DBMS. At this step, we can even choose a non-relational logical data model. But in general, entities and relationships are defined first at the conceptual level.

Physical data model is not the most concrete representation of your data model. This is a different way of looking at the nature of the data used in a problem domain. At this stage, you must select a specific technology (DBMS and platform) and use it to create a physical data model (physical data structure and some background logic) corresponding to the designed logical model. For relational logical data model we create tables and etc. For a network logical model we create collection s of records (nodes) etc. For a hierarchical logical data model we create a tree-like structure etc. SQL we can use for any actions (DML, DDL, DCL, TCL) for relational model. But other DBMS should provide these actions by their language.

A Star Schema is a data model that you can also look at from a conceptual, logical and physical point of view. Conceptually: this is a “fact” entity relateded with “dimension” entities. Logically: this is a hypercube, that you know how to rotate, slice, project, digitalize or aggregate, etc. Physically: this is structure, designed in specific DBMS. It could be relational (ROLAP) one, multidimensional (MOLAP) one, column one etc.

If we assume that the physical implementation of the Snowflake schema is also not necessarily relational (tables), then we cannot talk about normalization - this is only possible in relational DBMS, then it is better to define a Snowflake Schema is a model, where one dimension can be represented by more than one entity, but by several related ones. In a star schema, each dimension is only one entity.