Automatically infer table relationships to eliminate manual mapping

Automatically infer table relationships to eliminate manual mapping targets a $6.0B = 200,000 data-using organizations × $30K ACV total addressable market with medium saturation and a year-over-year growth rate of 25% — estimated growth for metadata/catalog/lineage and data governance markets (MarketsandMarkets/Gartner estimates for metadata management and data observability markets, 2022-2024).

Key trends driving demand: Trend — Enterprises are consolidating on cloud data warehouses and centralized metadata stores, creating a common integration point for automated relationship inference.; Trend — Advances in LLMs and tabular embedding models improve automated understanding of column semantics and relationship likelihoods, enabling high-accuracy inference.; Trend — Growing adoption of analytics engineering (dbt, modular pipelines) raises demand for automated generation of dbt models and join logic to speed time-to-insight.; Trend — Increasing regulatory focus on data lineage and provenance creates demand for automated, auditable mappings that can be validated and stored..