Table relationships with ambiguous paths treated differently between DQ and Import

[dmkblesser]

Hi Community, 

I have done some testings with my data model and I found that Power BI seems treat the ambiguous paths differently by the connection mode. 

1. data_range table uses effective_date to connect other tables, but for each effective_date, it also contains multiple start_date and end_date. That's why it is many-to-many to the framework table and daily nav table. 

2. active_framework table is the a table contains effective_date and frameworks, one effective_date may associate to multiple framework.

3. unique framework table queries the same table as active_framework from the source data but only selects the distinct value. 

4. daily_nav is my fact table. 

The reason I set up the bidirection between unique framework table and active_frame table is that I use the value from unique framwork table for a filter and I also use effective_date from date_range table for a filter. I want those two filters to be interactive. 

I understand that I can use visual level filter to aovid the bidirection or I can use the value from my fact table for slicer or I can have a true dim table with unique effective_date. My question is more related to the connection mode. With connecting to the same data source, I have no issue to establish the relationship shown in the picture. However, as soon as I made one of those table into Import mode, I receive the ambiguous path error message and it won't allow me to build the relationship.

For experimental purpose, I have aslo tried to have each of those table in its own semantic model, and within each semantic model I use IMPORT for each table. Then I create an additional semantic model to connect to those 4 semantic model but I still receiving the error message when I try to build the relationship. 

The source data is from the Azure Databricks, does anyone one know if Power BI engine somehow ignore some of the joins for DQ mode? I just don't understand why it works with DQ but not Import. 

[dmkblesser]

Thank you so much everyone. That really cleared some doubts for me. 

[v-kpoloju-msft]

Hi @dmkblesser, 

Thank you for reaching out to the Microsoft fabric community forum. Also thanks to @rohit1991, @Sandip_Palit, for those inputs on this thread.  

You're right in noticing different behaviour between Direct Query and Import modes. In Direct Query, Power BI is generally more permissive and defers complex relationship resolution to the source (like Databricks), allowing ambiguous paths and bidirectional filtering.

However, in Import mode, the Power BI engine requires a single unambiguous path for filters to flow, especially when bidirectional relationships and many-to-many joins are involved. This is why you're encountering the "Ambiguous relationship path" error after switching just one table to Import mode or when combining multiple semantic models.

Why this happens: Your model has multiple valid paths between filter tables (e.g., unique_framework ↔ active_framework ↔ daily_nav, or unique_framework ↔ daily_nav directly), and when Power BI cannot resolve a single path for propagation in memory (Import mode), it prevents the model from loading.

Option 1: Use a True Star Schema with Dimension Tables: Create a dedicated date dimension with only distinct effective_date. Use that date dimension in all relationships instead of sharing columns between fact and dimension tables. Eliminate bidirectional filtering unless absolutely needed try to control filter context via DAX measures or visual-level filters.

Option 2: Remove Ambiguity by Breaking Relationships: Replace the bidirectional relationship between unique_framework and active_framework with DAX-based filtering or use TREATAS() in measures to propagate the filter manually.

Option 3: Use Composite Models with Direct Query-to-Import Linking: If you still need to mix Import and DQ, try using composite models where your Import tables are brought in as a separate data source and linked via relationships – though you’ll need to ensure all table joins are 1:1 or many:1 to avoid ambiguity.

Kindly refer to the below mentioned documents links:

Ambiguous Relationships in Power BI – Docs

Optimize models with many-to-many relationships

Thank you for using Microsoft Community Forum.

[rohit1991 · Accepted solution]

Hi @Sandip_Palit ,

Why this happend

In DirectQuery, Power BI is more permissive

• Power BI pushes queries directly to the source (Databricks in your case).

• Since the source database is handling the joins, Power BI tolerates ambiguous relationships (like multiple active paths or many-to-many joins) that would otherwise violate Import model constraints.

• It assumes the source engine can optimize or resolve the join ambiguity.

 In Import Mode, Power BI strictly enforces relationship rules. Import mode loads data into Power BI's internal VertiPaq engine, which requires: 

In your diagram:

1. date_range ↔ active_framework and daily_nav → many-to-many via effective_date.

2. active_framework ↔ unique_frameworks → many-to-many via framework.

3. Bidirectional filters in both relationships.

This creates multiple ambiguous paths from date_range to daily_nav via different join paths — acceptable in DQ, rejected in Import.

How to Solve This

Option 1: Remove Ambiguity (recommended for Import mode)

• Create true dimension tables (with unique values for effective_date and framework).

• Use single active path (e.g., from date_range to daily_nav) and disable bidirectional filters.

• If needed, use TREATAS() or USERELATIONSHIP() in DAX to enable relationships selectively in measures.

Option 2: Use Composite Model (DQ + Import hybrid)

• Leave ambiguous tables in DQ mode if necessary (e.g., daily_nav).

• Use Import for lookup tables (unique_frameworks, date_range) with flattened or pre-aggregated values to reduce complexity.

• Be aware that cross-source relationships might require aggregation and not support all DAX patterns.

Option 3: DAX Workaround for Filters

If you still need interaction but can’t use bidirectional filters, use this pattern:CALCULATE(
[Measure], TREATAS(VALUES('Unique Frameworks'[framework]),'Active Framework'[framework]))

This manually propagates filters between non-related tables, mimicking bidirectional filtering without activating it.

[Sandip_Palit]

Power BI handles relationship paths differently in DirectQuery (DQ) and Import modes. When ambiguous relationships (such as many-to-many) exist, behavior can change depending on the connection type.

Here’s some recommended Solution Steps:
- Replace bi-directional relationships with single-directional ones where possible.
- Create a dedicated date dimension table with distinct effective_date values. Connect it as a true dimension to both active_framework and daily_nav.
- Remove direct connections between unique_framework and active_framework tables if not essential.
- Use DAX measures or USERELATIONSHIP() function to activate secondary relationships in specific visual scenarios.
- Use slicers based on dimension tables and apply cross-filtering through DAX if needed.
- Avoid using columns with multiple contextual meanings (effective_date, start_date, end_date) in ambiguous relationships.

Optional Workaround (if you must use Import mode):
- Flatten the model through Power Query by merging framework data with effective dates before loading into Power BI.
- Pre-aggregate or denormalize in Azure Databricks to reduce the need for complex relationships in Power BI.
- Ensure the model follows star schema principles with clean dimension-fact separation.

If this explanation and solution resolve your issue, please like and accept the solution.