Analytics Workloads
BI and ad-hoc analytical queries running on the same lakehouse that powers retrieval and agents — without a separate warehouse copy.
Most organizations duplicate data: a warehouse for analytics and a lake or object store for AI. The duplicates drift; the numbers stop matching. Running analytics directly on the lakehouse, with a query engine sized for interactive use, removes the duplicate.
What it solves
Eliminates the 'why does my dashboard say one thing and the agent another' problem at its source: there is no longer a separate AI copy.
How we build it
Trino, Starburst, Dremio, Athena, BigQuery external tables, or Snowflake external tables — whichever fits — sit on top of the same Iceberg or Delta tables retrieval reads from. BI tools query through the engine; performance tuning happens at the table and partition level.
- Interactive query engine on lakehouse tables
- Materialized views for hot dashboards
- Caching and result-set reuse
- Per-query cost attribution
What changes when it is in place
Dashboards and agents are reading the same row, at the same freshness, with the same access controls. When numbers disagree it is a real disagreement, not a sync delay.