The Problem: The Pitfalls of Direct Many-to-Many Relationships When working with unpivoted or multi-dimensional data—such as Survey review dimensions split against aggregated metrics—developers frequently connect entity tables directly using a Many-to-Many ($*$:*) relationship. While Power BI allows this natively, it introduces massive performance overhead and analytical limitations: Broken Filter Context: Direct many-to-many relationships can completely fail to evaluate row-level granularity, leaving matrix visuals unable to filter metrics properly across complex cross-sections. Performance Bottlenecks: Large-scale fact tables using bidirectional or direct cross-filtering face major processing delays, slowing down visual render times significantly.    The Solution: A Side-by-Side Comparison To illustrate the impact on report architecture, let’s look at how two different approaches handle an unpivoted service dataset. Approach 1: Direct Many-to-Many (The Anti-Pattern) In this model, the dimensions are forced into a direct relationship with the unpivoted fact table. The Result: The relationship engine struggles with cross-filtering directionality. Row-level metrics in complex matrices return blank or unseparated totals, preventing structural data exploration. Approach 2: Calculated Bridge Table (The Gold Standard) By introducing a centralized Bridge Table populated with unique keys, we resolve data ambiguity and create a clean Star Schema. Step 1: Create the Bridge Table via DAX Instead of complicating your Power Query ETL, instantiate a highly performant, single-column bridge table directly in your model: Step 2: Establish One-to-Many Relationships Join your primary metadata dimensions and your core fact tables to this new bridge table using clean One-to-Many ($1$:)* relationships. This aligns perfectly with VertiPaq engine optimizations.   Performance & Visual Impact Feature / Metric Approach 1: Many-to-Many Relationship Approach 2: Calculated Bridge Table Row-Level Matrix Filtering  Fails to properly isolate metrics across row fields. Isolates and computes metrics perfectly across rows. Data Engine Processing  Higher render load via Performance Analyzer. Optimized execution times via single-direction filters. Relationship Schema  Messy, ambiguous bidirectional filtering. Clean, predictable unidirectional Star Schema. Community Takeaway If you want to keep your data models highly performant, scalable, and mathematically accurate, stop relying on default Many-to-Many relationships. Take a moment to extract unique keys using a calculated DAX table, build a structural bridge, and let your filter context flow naturally. ... View more