Fundamental Concepts 1. What is Data Modeling, and why is it important? Answer: Data modeling is the process of creating a visual blueprint or conceptual structure of an enterprise’s data assets. It defines how data is stored, organized, accessed, and updated within a database or analytical framework. Why it matters: ● Prevents data redundancy and inconsistencies. ● Establishes clear business rules and data governance across engineering and business teams. ● Improves query performance and optimizes storage costs. 2. What are the 3 stages of Data Modeling? Answer: Data models evolve through three distinct phases, transitioning from high-level business concepts to low-level physical code: Stage
Main Target Audience
Primary Focus
Technology Dependence
Conceptual Model
Business Stakeholders & Analysts
High-level entities (e.g., Customer, Order) and high-level relationships.
Agnostic
Logical Model
Data Architects & Data Engineers
Entities, attributes, primary/foreign keys, and data types, independent of execution tech.
Agnostic
Physical Model
DBAs & Database Developers
Actual tables, columns, indexes, partitioning keys, views, and target DB engine constraints.
Vendor-Specific (e.g., Postgres, Snowflake)
3. What is the difference between OLTP and OLAP systems? Answer: