How are teams structuring Microsoft Fabric Warehouse for scalable ELT and reporting workloads?
I’m looking at architecture patterns for Microsoft Fabric Warehouse in environments where multiple data sources, transformation jobs, and Power BI workloads all depend on the same platform. A typical flow might look like: Source systems → ingestion → staging → transformations → Fabric Warehouse → semantic models → Power BI I’m particularly interested in how teams are handling: staging vs curated schemas ELT transformations inside the Warehouse incremental loading patterns data quality checks before publishing CI/CD across dev, test, and production workload separation for engineering vs reporting monitoring failed loads and long-running queries deciding when to use Warehouse vs Lakehouse for the same solution For teams running Fabric Warehouse at scale, what architecture patterns have worked best for keeping the platform maintainable and avoiding duplicated transformations?An Overview of Lakehouses and Data Warehouses
Unlocking the Future of Data: Lakehouses vs. Data Warehouses In today’s data-driven world, choosing the right architecture is crucial for turning information into insight. Are traditional data warehouses still the gold standard, or are modern lakehouses rewriting the rules? Dive into our latest article as we explore how these two powerful approaches stack up — and discover which one could transform your data strategy.
PowerBI with Snowflake released refresh error: 'Implementation' are not valid Snowflake options.
PBI Version: 2.149.1252.0 64-bit (November 2025) After I imported table from Snowflake, in PowerQuery source, there will show a param named Implementation="2.0" Then refresh in PBI Desktop succeeded, but after I release it to PBI server, it shows error while refreshing: 'Implementation' are not valid Snowflake options. Valid options are: 'ConnectionTimeout, CommandTimeout, CreateNavigationProperties, Role' Complete Error msg: Data source error: {"error":{"code":"DM_GWPipeline_Gateway_MashupDataAccessError","pbi.error":{"code":"DM_GWPipeline_Gateway_MashupDataAccessError","parameters":{},"details":[{"code":"DM_ErrorDetailNameCode_UnderlyingErrorCode","detail":{"type":1,"value":"-2147467259"}},{"code":"DM_ErrorDetailNameCode_UnderlyingErrorMessage","detail":{"type":1,"value":"'Implementation' are not valid Snowflake options. Valid options are: 'ConnectionTimeout, CommandTimeout, CreateNavigationProperties, Role'"}},{"code":"DM_ErrorDetailNameCode_UnderlyingHResult","detail":{"type":1,"value":"-2147467259"}},{"code":"Microsoft.Data.Mashup.ValueError.Reason","detail":{"type":1,"value":"Expression.Error"}}],"exceptionCulprit":1}}}
Snowflake Mirrored Tables Throttled for 24 hours
My Snowflake keeps randomly being throttled. At times this seems to last for 24 hours. We only load data into Snowflake once per day but the mirroring may not resume until several hours after the snowflake load jobs have completed. While I am ok with the mirroring being throttled to some extent, the fact that it does not resume until several hours after the load has completed in snowflake, is making the mirroring capability unreliable and of little use for us.
