Star schemas, medallion architecture, automated pipelines: built on Microsoft Fabric, Azure Data Factory, or Databricks. Data that's structured once and trusted everywhere.
Most dashboard problems aren't dashboard problems. They're warehouse problems wearing a Power BI costume. If the numbers underneath are inconsistent, no amount of visual polish fixes that.
We design warehouse environments built to scale, structured for query performance, with clean integration with Power BI and room to grow as data volume and complexity increase. Built on Azure Synapse, Microsoft Fabric, or Databricks, depending on what's already in your environment.
On Fabric specifically, that increasingly means Direct Lake mode, Power BI reading Parquet files straight from the lake, without importing or duplicating data into a separate model. It combines the speed of Import mode with the freshness of DirectQuery, without the drawbacks of either, and it's the default storage mode for new semantic models built on a Fabric Warehouse.
Pipelines move data from operational systems into the warehouse, applying transformations along the way. We build these as Fabric Dataflows Gen2 and Pipelines, Azure Data Factory pipelines, or Databricks Lakeflow Declarative Pipelines. Automated, monitored, and built to fail loudly rather than silently.
Medallion architecture throughout: Bronze for raw data landing exactly as it arrives, Silver for integrated, cleaned data that becomes the actual single source of truth, Gold for the presentation layer, dimensional models built for self-service reporting and analysis. The same proven pattern, on whichever platform fits your stack.
Raw data rarely arrives in a shape suitable for analysis. We model it properly: star schemas, fact and dimension tables, SCD logic where history needs tracking, using Kimball methodology rather than ad-hoc table sprawl.
This is the layer that decides whether your dashboards stay fast and trustworthy as data grows, or slowly become the thing nobody believes anymore. Whether that model lives in a Warehouse or a Lakehouse depends on the shape of your data and your team: structured, governed reporting points toward a Warehouse; large volumes of varied, less structured data and a Spark-first team points toward a Lakehouse. Both use the same SQL engine underneath, so it's rarely a one-way decision.
A pipeline that fails silently is worse than no pipeline at all. We build monitoring and failure-handling in from day one, so if a source system changes or a load fails, you find out immediately, not three weeks later when someone notices the dashboard looks wrong.
This extends to housekeeping most teams overlook. Microsoft stopped auto-creating default semantic models on new Fabric warehouses and lakehouses in September 2025, and as of November 2025 every existing default semantic model became a standalone item. If nobody's looked since, there's a good chance your workspace has orphaned semantic models quietly sitting there, worth auditing and cleaning up rather than leaving for someone to trip over later.
Every report and dashboard pulls from the same governed warehouse, so there's no more reconciling three different "revenue" numbers before a meeting.
Automated pipelines replace the Tuesday-morning spreadsheet ritual entirely.
Fabric, Azure, or Databricks: we work with your existing platform investment, not against it.
Medallion architecture and proper dimensional modelling are built to handle more sources and more volume without starting over.
That's exactly the kind of thing worth a conversation before committing to either. No pitch, just a clear look at where the real problem sits.
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