Hi All, I wanted to check if anyone connected Fabric to Dataverse (Power Apps). Which option you chose (Copy Job or Shortcut) ? Do we have to enable Link to MS Fabric from Power Apps for the Shortcut option to work ? Thank you !

Post your burning question about Microsoft Fabric in this thread below 👇 will try to answer as many as we can!

In our current project we decided to deploy with fabric cicd authenticated with a service principal and using the parameters.yml with find_replace for the environments. For now it looks pretty good but I am wondering if anyone else here is using this approach for deployment vs the deployment pipelines with rules, environment libraries etc. Related topic is how do you guys manage migrations as we needed to deploy in two phases in order to have some sql analytics endpoint views ready before deploying the Graphql API

Today, I decided to make a small benchmark between these 3 tools 🎯 Benchmark setup 2 data volumes: 100k and 5M rows 3 scenarios: Simple Intermediate (adding columns, filters, etc.) Advanced (joins, complex calculated columns, window functions, indexes…) 3 tools compared: Dataflow Gen2 Spark notebook T‑SQL in the warehouse The detailed results are shown in the attached chart.​ 📌 Key takeaways 1) T‑SQL is the clear winner Across all scenarios, it’s either the fastest or very close to the top. Even for complex transformations (window functions, aggregations, joins), the Fabric SQL engine is extremely efficient. 2) Dataflow Gen2: very sensitive to volume 100k rows: 35–41 s → acceptable. 5M rows: 39–105 s → execution time explodes. 3) Spark notebook: stable but never first Execution time is fairly consistent (~40–53 s), but it doesn’t dominate the advanced 5M‑row scenario like I expected. It’s also penalized by ~10 s of cluster startup… 4) Scale completely changes the picture At 100k rows, everything looks “fast” (3–41 s). At 5M rows, the gap grows to 1× vs 6× between tools. 5) CU consumption: similar Across the tests, all three options sit in a similar range of about 9–11 CU for the workloads. The big difference: if you don’t stop the Spark notebook, it keeps consuming CUs at the same level even when it’s just “waiting”, whereas T‑SQL and Dataflow Gen2 stop consuming once the workload is done. My personal verdict Strong SQL team → T‑SQL Fastest, most stable, and very competitive even for complex workloads. Need low‑code / Power Query team → Dataflow Gen2 Great tool, but you really need to watch Query Folding carefully. Need predictable scalability / data engineering use cases → Spark notebook Stable performance, ideal when you want to avoid surprises. Any of you already make this kind of benchmark ? What was your results ? Ps: if you want to support me, do not hesistate to share or like my linkedin post ;-)

I'm running Tabular Editor 2.24.1 in an Azure DevOps pipeline to validate Power BI semantic models using the -SC (schema check) flag, and I'm getting a schema validation error for one of my tables. Tabular Editor 2.24.1 (build 2.24.8878.22493) -------------------------------- Loading model... Loaded script: D:\a\1\s/repo_octopus/_DevOps/Scripts/SetConnectionStringFromEnv.cs Executing script 0... Trying to set connection for data source: 'SQLDW' using env var 'SQLDWConnectionString' Set connection string for data source 'SQLDW' Checking source schema... ##[error]Unable to retrieve column metadata for table 'Sales Order'. Check partition query. Does the -SC flag require the data source to be accessible from the build agent, or does it just validate syntax?​