Data Quality as Code in Fabric: Declarative Checks on Materialized Lake Views If you’ve ever shipped a “clean” silver or gold table only to discover (later) that it quietly included null keys, impossible dates, or negative quantities… you already know the real pain of data quality. The frustration isn’t that bad data exists. The frustration is that quality rules often live somewhere else: in a notebook cell, in a pipeline activity, in a dashboard someone checks (sometimes), or in tribal knowledge that never quite becomes a contract. Microsoft Fabric’s Materialized Lake Views (MLVs) give you a more disciplined option: you can define declarative data quality checks 

Your lakehouse doesn’t need more dashboards that claim the data is clean. It needs quality rules that run where your data is built—and signals that tell you when quality drifts. Use #MaterializedLakeViews to add declarative #DataQuality constraints to #MSFabric's lineage, with #PowerBI reports!

The Advanced Lakehouse Data Product: Shortcuts In, Materialized Views Through, Versioned Schemas Out There’s a familiar tension in modern analytics: teams want data products that are easy to discover and safe to consume, but they also want to move fast—often faster than the governance model can tolerate. In Microsoft Fabric, that tension frequently shows up as a perception of workspace sprawl. A “single product per workspace” model is clean on paper—strong boundaries, tidy ownership, straightforward promotion—but it can quickly turn into dozens (or hundreds) of workspaces to curate, secure, and operate. This post proposes a different pattern—an advanced lakehouse approach that treats the lakehouse itself like a product factory:

A “workspace per data product” sounds clean—until you have 60 #DataProducts. This advanced lakehouse pattern uses shortcuts + #MaterializedLakeViews + versioned schemas to deliver left-shifted data products with #OneLakeSecurity, while keeping the perception of #MSFabric sprawl under control.

Materialize Responsibly: How Fabric’s External Data Materialization Affects “Zero Unmanaged Copy” — and Where Materialized Lake Views Now Shine Microsoft Fabric’s Warehouse can now materialize external files into tables with straight‑ahead T‑SQL, and Materialized Lake Views (MLVs) have quietly leveled up with optimal refresh (including incremental) and stronger, UI‑backed monitoring. If your north star is Zero unmanaged copy, the question isn’t “should I materialize?”—it’s “how do I materialize responsibly under OneLake governance?” Here’s what changed since our last take—and what to use when.

Materialize responsibly. #MicrosoftFabric’s Warehouse can land files in seconds—and now #MaterializedLakeViews add optimal refresh (incremental/full/skip) with #DataQuality and #Lineage. Here’s how to stay true to zero unmanaged copy with #OneLakeSecurity and Outbound Access Protection in the loop.

Materialized Lake Views (MLVs) in Microsoft Fabric A Materialized Lake View (MLV) is a table in your Fabric lakehouse that’s defined by a SQL query and kept up‑to‑date by the service. You write one CREATE MATERIALIZED LAKE VIEW … AS SELECT … statement; Fabric figures out dependencies, materializes the result into your lakehouse, and refreshes it on a schedule. Today, MLVs are in preview, SQL‑first (Spark SQL), and designed to make Medallion layers (Bronze → Silver → Gold) declarative instead of hand‑assembled pipelines.

In #MSFabric, one of my favorite new technologies are #MaterializedLakeViews. MLVs are currently in preview, but you can already see the potential to completely change the way #DataEngineers and #DataArchitects interact with and build multi-layer data architectures. The idea of #ZeroUnmanagedCopies…