What problem does DeltaForge solve?

Most business data is still relational in shape, and many lakehouse jobs are selective, frequent, or metadata-heavy. Those workloads benefit from direct execution rather than workers exchanging data across a distributed query plan.

Is distributed execution available?

Yes. Distributed execution exists for workloads where it genuinely helps, but it is opt-in. The default is direct execution on a single worker.

Where does DeltaForge sit in the lakehouse landscape?

Between a distributed cluster engine and a single-process embedded engine. DeltaForge runs concurrent native agents on Delta and Iceberg tables in place.

Why DeltaForge exists

Most business data is still relational: tables, joins, aggregates, operational records. Putting it in a lakehouse doesn't mean every job needs a distributed engine. Selective, frequent, and metadata-heavy workloads run faster with direct execution.

DeltaForge is built for this middle ground.

It runs native agents directly on Delta Lake and Apache Iceberg, scaling across workers only when a job actually needs it.

Pick the view that fits

Same product, two lenses.

For Developers

Single binary, standard SQL, native ODBC/JDBC/Python/R/.NET drivers, MCP for AI tools, and a property-graph layer over the same Delta and Iceberg tables.

Read the developer view

For Executives

Query the lake directly, pay only for active compute, verify every number against Apache Spark. Fewer systems, lower bills, data you actually own.

Read the executive view