Cut Text2SQL token costs up to 81%, and stop paying for wrong answers: a Neo4j semantic layer across BigQuery and Databricks

A Neo4j-based Neocarta semantic layer is presented as a metadata-only graph that helps Text-to-SQL agents discover relevant tables, columns, and join paths across large, messy data catalogs without repeatedly scanning warehouse schemas. Unlike metric-focused semantic layers that define certified measures over curated data, this approach emphasizes cross-platform schema discovery by ingesting warehouse metadata, descriptions, foreign-key relationships, sample values, and embeddings into a graph while leaving actual data in the warehouse. In benchmarks on a 278-table BigQuery Census catalog and a 264-table Databricks legacy lakehouse, graph-based retrieval reportedly improved reliability for smaller models and reduced token use, latency, and warehouse catalog queries for larger models; compact retrieval on Census reduced tokens by 45–81%, while Databricks tests showed 19–54% token reductions across eight Anthropic models. The approach uses semantic search to locate an anchor table and graph traversal to supply related tables and join details, allowing agents to generate SQL after limited retrieval rather than brute-force exploration. The post argues that such a graph can complement rather than replace existing governed metric layers, potentially expanding into an enterprise knowledge layer containing glossary terms, ownership, governance metadata, usage-derived relationships, and agent memory.

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