What breaks data teams
Tool sprawl creates uncertainty.
Data teams don’t fail because of SQL. They fail because the workflow is scattered and unreproducible. Xalorra compresses the workflow into stable contracts and versioned artifacts.
Too many tools, no single truth
SQL here, transforms there, profiles somewhere else, and nobody trusts what is “current”.
Notebook drift & fragile scripts
What worked yesterday fails today. The team spends time babysitting instead of shipping.
Governance becomes an afterthought
Tenant boundaries, auditability, and lineage get bolted on late—usually after incidents.
THE SHIFT
Move from “a collection of scripts” to a workflow with stable contracts: versioned datasets, controlled writes, profiling, and a serving surface that doesn’t surprise you.
Make data workflows predictable.
Then everything downstream gets easier.
Query & transform on DuckDB + Parquet.
Materialize versioned datasets you can trust.
Profile quality so teams ship with confidence.
A solution built for data teams
A platform workflow that stays sane as you scale.
Keep the lakehouse simple: DuckDB + Parquet, versioned outputs, controlled writes, and clear contracts. You can iterate quickly without sacrificing governance.
Lakehouse workflow
SQL, transforms, materialize, profile—all behind stable contracts.
Versioning & lineage
Treat datasets and outputs as artifacts. Reproducible today and explainable later.
Tenant isolation
Boundaries enforced by default patterns—no “remember to add filters” footguns.
Serve endpoints (Beta)
Stage control and rollbacks evolve, while artifacts and contracts stay disciplined.
What this unlocks
Cleaner datasets make ML training stable. Stable datasets make RAG grounding reliable (Beta). Serving becomes predictable. Teams stop fighting the workflow and start shipping.
SQL
Transform
Materialize
Profile
Artifacts
The workflow
From raw data to production behavior.
This is not a new “data concept.” It’s execution discipline: controlled writes, versioned outputs, and quality visibility—so teams can standardize without slowing down.
Prefer to click around first? Open Studio.
SOLUTION FLOW
A compact workflow teams can actually standardize.
01
Query & inspect
Run SQL against tenant-scoped Parquet and learn the dataset shape fast.
02
Transform
Apply deterministic transforms that can be rerun—no notebook drift.
03
Materialize versions
Write outputs as versioned artifacts so downstream stays stable.
04
Profile quality
Surface schema, row stats, and quality signals before shipping.
05
Serve & iterate (Beta)
Promote versions with evolving stage control—contracts stay stable.
WHY IT WORKS
Versioned outputs reduce churn. Profiling reduces surprises. Tenant isolation reduces risk. Stable contracts reduce integration pain.
Looks familiar? Good.
The best workflows are boring: repeatable, inspectable, and easy to teach to new team members.
FOR DATA TEAMS
Standardize the workflow. Keep the stack.
You don’t need to replace everything. You need stable contracts and versioned artifacts teams can rely on. That’s how data work stays explainable, not fragile.
Start with a dataset
Upload once. Query, transform, materialize, and profile. Then build ML or RAG (Beta) on top—without rewriting your surface area.
Prefer to explore? Open Studio.