Problem
Fabric ontology can be generated from a semantic model with one click — but customers still have to build the semantic model manually. For a lakehouse with 100–200 Gold tables, that's days of click-by-click work to select tables, define relationships, mark date tables, and create measures.
Customers with 5,000+ tables and 300–800 ontology candidates often estimate 3–4 weeks of manual effort before they can even start with ontology. This is a major adoption blocker for Fabric IQ.
What would help
Any form of automation or AI-assisted bulk creation of semantic models directly from lakehouse metadata — using schema, profiling, lineage, or Copilot — would dramatically shorten the path to ontology. Even partial automation (e.g., relationship detection on user-selected tables) would be a meaningful first step. Anyway to automate the creation ? not fully but a base atleast ?
Why it matters
Snowflake (Semantic Views Autopilot), Databricks (Genie), dbt, and AtScale already automate this. Fabric is behind on this specific capability, despite having stronger downstream features (ontology graph, Activator, agents). Closing this gap would accelerate Fabric IQ adoption for the enterprise segment with the largest data estates.
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