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Power BI Data Model Best Practices
Most Power BI reports that feel slow, produce wrong totals, or collapse the moment a new slicer is added share one root cause: a weak data model.
Category
Data Modeling
Star schema, relationships, and model design.
13 tutorials in this category
The model is the part of Power BI that is hardest to change later and easiest to get wrong early. A star schema — fact tables of events, dimension tables that describe them, one-to-many relationships flowing downhill — is not a style preference. It is the shape that lets the VertiPaq engine compress well and lets DAX measures stay short enough to read.
Start with the star schema guide and the relationship cardinality explainer, then the many-to-many and bidirectional material. Those two topics account for most of the models that look fine until a filter behaves unexpectedly, and both are easier to reason about once the one-to-many baseline is solid. Incremental refresh and aggregation belong here too, but they are scale decisions rather than design decisions.
The pattern that causes the most trouble is using bidirectional filtering to make a visual work instead of fixing the relationships underneath. It usually does make that one visual work. It also introduces ambiguity into every other filter path in the model, and the cost shows up later, in a report that is slow for reasons nobody can locate.
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Power BI Dataflows Explained (vs Datasets)
Dataflows and datasets are both storage layers for Power BI data, but they sit at different points in the architecture and solve different problems.
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Incremental Refresh in Power BI: Setup and Gotchas
Incremental refresh splits a fact table into history and a hot window. How partitions are created, and the limits that change the design.
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Snowflake Schema vs Star Schema in Power BI
Dimensional modeling is the backbone of a fast, maintainable Power BI dataset.
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VertiPaq Analyzer: Optimize Your Power BI Data Model
Every Power BI Import model is stored in the VertiPaq engine, a columnar, in-memory database.
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Role-Playing Dimensions in Power BI: One Date Table
One date table, three roles: order, ship, due. The three ways to model role-playing dimensions without breaking relationships.
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Import vs DirectQuery vs Dual vs Direct Lake
Four storage modes, one decision. When Import, DirectQuery, Dual or Direct Lake is right, with a matrix covering speed, cost and freshness.
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Power BI Row-Level Security: Control Who Sees What Data
Static and dynamic RLS with USERNAME(), deployment to the service, and the model patterns that scale to multi-tenant reports.
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Power BI Performance Optimization: Reports and DAX
Find the bottleneck before optimising: Performance Analyzer, then DAX, then the model. A diagnosis order that saves hours of guessing.
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Calculated Tables and What-If Parameters in Power BI
Build tables with DAX instead of a source, and turn static assumptions into What-if slicers. Five patterns, from date tables to scenarios.
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Bidirectional Filtering in Power BI: When to Use It
Bidirectional filtering solves some models cleanly and breaks others. The three valid cases, the ambiguity it creates, and the warning signs.
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Star Schema Data Modeling: Fast Power BI Reports
Fact vs dimension tables, relationship cardinality, and why bidirectional filters usually make a model slower instead of better.
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One-to-Many vs Many-to-Many Relationships in Power BI
Cardinality decides how filters travel. One-to-many is the safe default; many-to-many is sometimes needed but costs clarity and speed.