Intelligent data mapping.
From documentation to execution.
The Source-to-Target Mapping Platform that closes the gap between business rules and SQL — so your data teams stop guessing and start delivering.
400–600 hours. Per project. Spent on manual specification.
- 400–600h: Manual spec per project
- 15–25%: Rework from translation errors
- 200–400h: Audit prep per cycle
- 40–80h: Per change request
The cost your team absorbs before a single row of data moves.
THE PROBLEM
Static specs diverge from reality the moment they're written.
The same three problems. Every project. Every time.
400–600h
Manual specification bottleneck
Every mapping is rebuilt in Excel from scratch — fragmented across spreadsheets, email, and Jira with no source of truth.
15–25%
Rework from miscommunication
Analysts write rules, developers interpret them differently — and up to a quarter of project effort goes to avoidable rework.
200–400h
Audit burden with no traceability
Audit prep means manually reconstructing decisions that should have been captured automatically from day one.
THE DIFFERENCE
A workflow analysts and engineers actually agree on.
WHO IT’S FOR
Built for the people who actually build data warehouses.
Data & business analysts
Rebuilding mapping specs in Excel for every project, waiting to see how developers interpret them.
Design visually, approve in-platform, and watch SQL generate from your logic.
Data engineers & developers
Receiving ambiguous specs and spending up to 25% of project time on avoidable rework.
Precise, approved mappings arrive with auto-generated SQL — just execute.
Data managers & compliance
Reconstructing column lineage manually from emails and Excel before every BCBS 239 or GDPR audit.
Immutable versioned history means you’re always audit-ready — zero reconstruction.
HOW IT WORKS
Four steps from requirement to production-ready SQL.
No email threads. No version confusion. No guesswork.
1
Visual design
Build source-to-target mappings on a graph canvas — no coding required.
2
In-platform approval
Stakeholders review and sign off inside ATLAS — no email threads.
3
Auto SQL generation
One click produces production SQL for Oracle, PostgreSQL, or SQL Server.
4
Perfect alignment
Column lineage and versioned history keep every requirement traceable to production.
Enterprise-grade architecture
Built to fit your infrastructure.
Not the other way around.
Not the other way around.
Presentation: React SPA
Application: Python / Flask
Data: PostgreSQL / JSONB
Security: Azure AD / OIDC with RBAC
Isolation: Single-tenant architecture
Deployment: On-premises or Docker / Kubernetes
Connectivity: JDBC / ODBC integration
KEY CAPABILITIES
Four capabilities that replace the manual specification workflow.
Visual Dataset Builder
Graph-based canvas with drag-and-drop mapping, smart schema awareness, and reusable artifacts — no coding needed.
Column lineage
Every column tracked automatically, version-controlled, and built for BCBS 239 and GDPR audits.
Auto SQL generation
Visual mappings produce dialect-ready SQL via SQLGlot — or reverse-engineer legacy SQL into visual form with SQL Import.
Auto documentation
Specifications are generated and updated automatically as mappings change — they never go stale.
Outcomes
Measurable results from the first 90 days.
40%
Faster project delivery
Across all pipeline phases
60%
Fewer errors in production
Driven by aligned specifications
90%
Reduction in audit prep
400 hrs → 40 hrs per cycle
75%
Faster onboarding
3 months → 3 weeks
90%
Faster impact analysis
80 hrs → 8 hrs per change
€1.2–1.8M
3-year total value
Spec, audit & delivery gains