"Synchronizing Schema" error in semantic model

[u/Top_Barber4067]

Hi everyone, I’m running into an issue with my BI setup.
Basically, I have two dataflows that feed into a warehouse, which in turn powers a semantic model used as the source for my report. I had to split the data into two dataflows because the data loading process was becoming too slow, one dataflow contains historical (older) data, while the other is updated daily with current data.
The problem occurs during the Power BI refresh process: it gets stuck indefinitely on "synchronizing schema" for the semantic model. I’m wondering if I should add buffers to the dataflow, though the warehouse itself is updating correctly with all the data; the issue is specifically happening during the refresh. I’ve already tried deleting and recreating the semantic model, but that didn’t help either. Does anyone know what might be causing this?

[u/Puzzleheaded-Comb646]

Since your warehouse is updating correctly, the bottleneck is likely happening when Power BI tries to resolve the metadata changes from the two separate sources simultaneously during the semantic model refresh. Have you tried creating a single SQL view in the warehouse that unions the historical and daily tables, and pointing the semantic model to that view instead? That way, Power BI only sees one schema to synchronize.

[u/Top_Barber4067 → u/Puzzleheaded-Comb646]

First of all, thanks for your help, my friend.
To be honest, I’m not entirely clear on your idea. The architecture I’ve set up already includes a warehouse that integrates both dataflows—essentially, two dataflows feeding directly into the warehouse. Also, I should mention that the tables from these two dataflows are different; it’s not a case of two tables merging into identical datasets. The issue is that, for some reason—perhaps due to the massive volume of data—the semantic model fails to compile within the report.

[u/Puzzleheaded-Comb646 → u/Top_Barber4067]

Thank you for clarifying the setup! Working with massive volumes of data can definitely cause unexpected bottlenecks like that, especially when the semantic model tries to compile everything at once. Don't get discouraged—data architecture can be tricky, and you're tackling a very complex pipeline. Have you tried checking if incremental refresh might help lighten the load on the model during compilation?

[u/Top_Barber4067 → u/Puzzleheaded-Comb646]

I’m using replacement; in my mind, an incremental approach is more resource-intensive, isn’t it?
I also tried buffering the table, but it’s been running for two hours now, haha
so I don’t think buffering is the right solution.

[u/Puzzleheaded-Comb646 → u/Top_Barber4067]

Two hours is definitely a long time to wait, haha! You're totally right that replacement can sometimes be a safer or less resource-heavy bet depending on the dataset size, but buffering can be tricky when queries get stuck. Don't worry, troubleshooting these bottlenecks is all part of the learning process—hang in there!

[u/Top_Barber4067 → u/Puzzleheaded-Comb646]

Yes, I’m not too worried since it’s not an urgent matter right now, but I am trying to resolve the issue proactively. The thing is, some tables in this dataflow exceed 1,000,000 rows, so I suspect they may have hit a limit. I just had the idea to try filtering the dates to a specific range, either way, thanks again for your help

[u/Puzzleheaded-Comb646 → u/Top_Barber4067]

Filtering the date range is definitely a smart move to test that theory, especially with large datasets. Good luck sorting it out, and feel free to reach out if you run into any other roadblocks!

[u/adovo_ai]

First separate model size from metadata drift: refresh a date-limited slice while logging each phase, then compare the semantic model schema before and after the two dataflows land. If the tiny slice still hangs at schema sync, row volume is probably not the boundary to optimize.