OQEN

Trusted lineage and impact before risky warehouse changes

Understand blast radius before your next warehouse change.

OQEN helps data teams answer dependency and impact questions before schema changes, migrations, and redesign work. Recommendations stay advisory and human-reviewed.

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Start with one active change request and see what depends on it, what could break, and what needs review before implementation.

Interactive parser teaser

See one change request turn into trusted lineage and impact context.

Mock data shows how OQEN combines warehouse metadata and pipeline logic before a redesign decision.

Teaser details are collapsed. Expand to review scoped samples and preview interaction.

Pilot-safe proof

>= 30%

Target reduction in redesign decision cycle time

>= 0.90

Target blast-radius prediction accuracy

Scoped pilot

One blocked redesign decision to validate

Measured in a scoped pilot against your current redesign decision baseline, with outcomes reviewed by your delivery team before rollout.

Why OQEN

Stop reverse-engineering dependencies before every change.

Teams lose time pulling engineers into dependency checks across schemas, SQL, DAGs, Python ETL, and tribal knowledge. OQEN reconstructs one trusted view before change approval.

Dependency checks pull engineers into manual investigations

Reconstruct one view across schemas, SQL, DAGs, and Python ETL so teams stop spelunking before every change.

Blast radius stays unclear until late

Answer lineage and impact questions before redesign, migration, or schema change commitments.

Redesign decisions are hard to defend

Keep guidance cited, advisory, and human-reviewed so architects, platform leads, and reviewers can approve change with context.

How it works

From fragmented context to review-ready decisions.

  1. 01

    Ingest metadata and pipeline logic

    Capture the warehouse metadata and workflow logic behind the change.

  2. 02

    Build trusted lineage and impact context

    Create one view of dependencies, transformations, and likely blast radius.

  3. 03

    Review guidance before implementation

    Use AI-assisted explanations and design options as advisory input, not auto-applied change.

Human-reviewed guidance

AI recommendations stay advisory and human-reviewed. Teams review guidance before implementation, not as auto-applied change.

Start with one blocked redesign decision.

Book a guided demo for one active migration, schema change, or model-rationalization decision. Use self-serve mode if you want to explore first.

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