fabric-mlvlisted

# Fabric Materialized Lake Views (MLV) Declarative SQL/PySpark transformations that persist as Delta tables in a schema-enabled lakehouse. Fabric handles refresh strategy, dependency order, and data quality enforcement so you don't write notebook orchestration. ## When to use vs not Use MLVs for medallion bronze→silver→gold pipelines, frequently-queried aggregates, declarative data quality, and reporting datasets that need automatic refresh. Skip them for one-off queries, sub-second streaming (use Real-Time Intelligence), or transformations that need ML inference / external API calls / non-SQL Python (use a regular Spark notebook). ## Prerequisites - **Schema-enabled lakehouse** — required. `enableSchemas` is immutable per lakehouse; you can't retrofit it. - **Fabric Runtime 1.3** — earlier runtimes can't author MLVs. Upstream still names 1.3 exactly (checked 2026-08-29), and **has not** extended the prerequisite to **Runtime 2.0** (GA Aug 2026 — Spark 4.1, Delta Lake 4.2, Python 3.13), so treat 2.0 as unverified for MLV authoring rather than assumed. This has a deadline: 2.0 is planned to become the default for new workspaces and environment items in **late September 2026**, at which point a new workspace stops defaulting to a runtime MLVs are documented against. Re-check the prerequisite then. - **Region** — not available in South Central US (as of 2026-04). - **CDF on source tables** — required for incremental refresh: `ALTER TABLE bronze.x SET TBLPROPERTIES (delta.en