In Power BI, joins merge tables in Power Query while relationships link separate tables in the data model, enabling cross-filtering without physically combining data.

SQL joins come in inner, left, right, full outer, left anti, and right anti varieties, and Power Query mirrors these with Inner, Left, Right, Full, and anti joins to identify non-matching records.

Power Query’s merge operation produces a single table based on a matching column, with common join types mirroring SQL’s—Inner, Left, Right, Full, and anti joins for non-matches.

Pro tips include using role-playing dimensions—such as a Date table serving multiple contexts—and avoiding circular dependencies that cause errors.

Cross-filter direction generally should be single-direction, with bi-directional filtering used sparingly to prevent performance issues and circular dependencies.

Best-practice guidance favors single-direction filtering as the default and recommends using CROSSFILTER() only when necessary to control filter paths.

Cross-filter direction typically flows from the One side to the Many side; bi-directional filtering should be avoided unless essential due to performance concerns.

A flat table is a single, all-encompassing dataset, useful for small data tasks and beginner-friendly scenarios.

Star Schema is preferred for clear, performant data modeling, while Snowflake and Flat Tables involve trade-offs in data size, complexity, and query speed.

Compare Star, Snowflake, and Flat Table designs across normalization, table count, DAX complexity, query speed, and storage to guide the right choice for the use case.

Schemas organize data with Star Schema centered on a fact table and dimension tables, and Snowflake Schema normalizes dimensions into related tables.

Core goals of Power BI data modeling include reducing redundancy, enabling fast queries, ensuring correct aggregations, simple DAX, and maintainability.