Power BI / Fabric deployment pipelines currently do not support semantic models that include native queries, as documented here: https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/understand-the-deployment-process?tabs=new-ui#semantic-model-limitations This limitation has a significant impact on enterprise BI architectures, especially when working with large cloud data warehouses such as Snowflake, Synapse, or BigQuery. Why this is a real problem Native queries are often mandatory, not optional, to: Preserve query folding when Power BI parameters break folding in M Push filters and logic down to the warehouse for performance and cost control Build parameterized semantic models (environment, scope, tenant, time windows) Avoid large in-memory transformations in Power BI Removing native queries in these scenarios is not a viable solution without serious performance degradation. Impact on CI/CD adoption Because of this limitation: Semantic models cannot be deployed via pipelines Teams must exclude datasets from pipelines or deploy them manually CI/CD becomes partial, fragile, and inconsistent Advanced, well-designed models are penalized, while simpler ones work This strongly limits the adoption of deployment pipelines in mature BI teams. Why this should evolve Deployment pipelines are presented as the standard CI/CD mechanism for Power BI and Fabric. To be enterprise-ready, they need to support semantic models that rely on native queries, or at least provide an explicit and safe opt-in mechanism. Call to action If you rely on: Large datasets Cloud data warehouses Parameterized or performance-critical semantic models Proper CI/CD practices ๐ Please vote for this idea. This evolution would greatly improve enterprise adoption of Fabric deployment pipelines.
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