POWER BI- Schema and Data Modelling.
Source: Dev.to
Overview
Schema and data modeling in Power BI is essential for performance and accurate reporting. Good modeling ensures clean, organized data that supports intuitive reports, fast query execution, and reliable aggregations.
Key Concepts
Star Schema
A star schema is a multi‑dimensional data model used in data warehouses and relational databases. It denormalizes data into fact and dimension tables, with the fact table at the center and foreign keys linking to dimension tables.
Benefits
- Clean, organized data makes reports intuitive
- Fewer joins enable faster queries on large datasets
- Simple queries with minimal join dependencies
Snowflake Schema
A snowflake schema extends the star schema by normalizing dimension tables into sub‑tables, creating branching structures (multiple interconnected stars).
Benefits
- Fast data retrieval
- Enforces data quality
- Common data‑warehousing model
- More storage‑efficient
Fact Table
The fact table is the central table in star and snowflake schemas. It stores quantitative data (measures/metrics) such as units sold, amount, etc., and links to dimension tables through foreign keys.
Dimension Table
Dimension tables store descriptive attributes (e.g., time, product) that provide context for the facts. They typically use a unique surrogate key (often an integer) as the primary key, which is referenced by foreign keys in the fact table.
Why Good Modeling Matters in Power BI
- Optimized memory usage – reduces the dataset’s memory footprint.
- Faster DAX query processing – improves report responsiveness.
- Accurate aggregations – ensures calculations reflect the intended business logic.
- Scalability – prevents performance degradation as data volumes grow.
Poor models can lead to slow performance, high memory consumption, and scalability issues.