Video by: Reid Havens
Every report hitting your raw tables runs the same expensive joins and aggregations over and over, even when the underlying data hasn't moved. Materialized Lake Views in Microsoft Fabric pre-compute those results once and store them as Delta tables in OneLake, so consumers read finished numbers instead of recalculating from scratch.
In this video I cover what MLVs actually are, how the automatic refresh logic decides between skip, incremental, and full rebuild, the Spark SQL syntax to create and manage them, and where they sit in a medallion architecture (think of them as a "Gold+" layer).
The part worth sticking around for: point Direct Lake at an MLV instead of your raw tables and you get pre-aggregated data at import speed. In the example here, a 50 million row fact table collapses to around 500K. Fewer rows means faster transcoding into VertiPaq and a lot less DirectQuery fallback risk. That's the single best reason to pair the two.
I also get into when NOT to use them, because they're not free. Sub-minute volatile data, tiny tables, and row-level security needs are all cases where an MLV is the wrong tool.
