Stop “data prep” whiplash.
Build clean, versioned datasets you can trust.
Query Parquet with DuckDB for fast iteration.
Transform and materialize outputs with reproducible versions.
Profile quality so downstream ML/RAG is predictable.
Designed for multi-tenant lakehouse workflows
SQL, transforms, and materialization—without breaking governance.
Tenant-safe by default
Keep data isolated across orgs with consistent access patterns and predictable contracts.
Versioned outputs
Every materialized dataset becomes a stable artifact you can build ML/RAG on top of.
Production-shaped behavior
The workflows you test in Studio match how your backend runs—no fragile notebooks.
Lakehouse Core
DuckDB execution over versioned Parquet. Transform pipelines that materialize clean datasets. Profiling that turns messy inputs into predictable downstream behavior.
DuckDB
Parquet
Transform
Materialize
Profile
Query like an analyst
Teams that query datasets
Run SQL on tenant-scoped Parquet with predictable performance.
Stay safe: guardrails and limits keep Studio responsive.
Build confidence with stable response contracts for apps.
Prefer to explore first? Open Lakehouse.
Transform like an engineer
Teams that transform data
Turn raw uploads into clean tables with deterministic transforms.
Keep changes reproducible—no notebook drift.
Route writes through controlled pipelines for governance.
Prefer to explore first? Open Lakehouse.
Materialize what matters
Teams that materialize outputs
Create versioned datasets you can train ML or build RAG on top of.
Track what changed, when, and why across versions.
Keep the lineage clear so teams can ship with confidence.
Prefer to explore first? Open Lakehouse.
Lakehouse you can operate
Make datasets predictable—before ML and RAG depend on them.
If you care about tenant isolation, stable contracts, and versioned outputs, you want a workflow that behaves like production from day one.