Mastering data modeling concepts

In this section, we will explore in detail essential data modeling concepts that every data engineer must be familiar with. Data modeling is the process of structuring and organizing data to represent real-world entities, their attributes, and the relationships between them. A solid understanding of data modeling concepts is crucial for designing efficient and accurate databases.

The following diagram depicts three different data models that can be found in a database:

Figure 9.5 – Three different types of data models

Let’s look at these in more detail:

• Conceptual data model: The conceptual data model represents high-level business concepts and relationships without concerning itself with implementation details. It focuses on capturing essential entities, their attributes, and the associations between them. The conceptual data model serves as a communication tool between data engineers, stakeholders, and domain experts to ensure a shared understanding of the business requirements.
• Logical data model: The logical data model provides a more detailed representation of the data, refining the conceptual model into a structure that is closer to implementation. It involves translating the conceptual model into a set of entities, attributes, and relationships. The logical data model aims to capture the semantics and meaning of the data in a technology-agnostic manner. Common techniques used in logical data modeling include ERDs and Unified Modeling Language (UML) diagrams.
• Physical data model: The physical data model focuses on the actual implementation of the database design, considering the specific database management system (DBMS) and its features. It defines tables, columns, data types, constraints, indexes, and other implementation details. The physical data model aims to optimize performance, storage efficiency, and data integrity based on the requirements and constraints of the target DBMS.

By mastering these data modeling concepts, including conceptual, logical, and physical data modeling, as well as ERDs, normalization, advanced modeling techniques, and data modeling tools, data engineers can create well-structured, efficient, and scalable databases that align with business requirements.

Now that we have covered database design fundamentals, we will test your understanding with sample interview questions.