From connection
to confidence.
A full data-comparison run, from connection to reconciled results. AI does the heavy lifting; your team reviews and approves at every gate.
One platform,
every data-trust problem.
Each use case ships with prebuilt validation templates, so your team starts in minutes, not sprints.
ETL testing for lakehouses
Validate transformations and loads across lakehouse formats and warehouses, from Iceberg and Parquet to object storage and the modern data stack, before bad data reaches production.
Data reconciliation
Prove source and target agree, across systems and databases, without hand-writing a single reconciliation script. The Smart Diff engine finds and classifies every mismatch.
Migration testing & reconciliation
De-risk a migration end to end: pre- and post-load checks plus full source-to-target reconciliation, so you cut over with evidence instead of anxiety.
Data readiness for ML & AI
Certify that the datasets feeding your models are complete, consistent and trustworthy, with a readiness score, so your AI initiatives build on solid ground.
A closer look
at the platform.
Everything DataGinii does to prove your data moved correctly.
Unified metadata catalog
DataGinii ingests your metadata and builds a single, searchable catalog: profiling columns, detecting PII, and surfacing quality signals. AI enriches it with clear descriptions, so your whole team speaks the same language about the data.
- Automatic schema discovery and column profiling
- AI-enriched descriptions and PII detection
- A single pane of glass across every source
AI-generated mapping specs
DataGinii proposes source-to-target column mappings from your metadata and profiles, each with a confidence score, and flags anything it is unsure about. You review, adjust and approve. Every mapping spec is versioned and human-signed-off.
- Confidence-scored column-pair suggestions
- Transformation-aware mappings for non-1:1 columns
- Versioned and human-approved before use
AI-built test plans & suites
Starting from prebuilt validation templates, DataGinii generates complete test plans, builds the executable test cases, and assembles them into suites: the exact process your team already follows, done for you in seconds.
- Test plans, test cases and suites generated for you
- Assembled from prebuilt validation templates
- Editable and approvable in DQ Studio
Out-of-the-box templates
A rich, growing library of dialect-aware validation templates covers the checks teams run every day: row counts, nulls, uniqueness, referential integrity, freshness, distributions and more. You start validating immediately instead of writing SQL.
- Dialect-aware checks that adapt to your engine
- Covers the everyday validation patterns out of the box
- Extend with your own custom rules
DQ Studio
DQ Studio is the authoring workspace where test plans, mapping specs, test cases and suites come together. Review AI-generated work, assemble validation flows visually, and orchestrate runs, all from one place, without touching the underlying tools.
- Visual assembly of validation flows
- Review and approve AI-generated artifacts
- Orchestrate runs from a single workspace
Smart Diff engine
Purpose-built to diff large datasets in seconds, the Smart Diff engine compares source and target row by row and classifies every difference it finds, from value mismatches and missing rows to case and precision differences, so you know exactly what changed and where.
- Cross-system, key-joined row comparison
- Every mismatch classified, not just counted
- Built to diff large datasets fast
Scales to your data volume
DataGinii scales to match your data volume: add workers to scale out horizontally, or bigger nodes to scale up vertically. From a handful of tables to billions of rows, throughput grows with your infrastructure. No re-architecting.
- Horizontal scale-out across workers
- Vertical scale-up on bigger nodes
- Throughput that grows with your data
Drill-down reporting
Multi-level reporting takes you from a project-level pass/fail summary down through suites and tables to the individual mismatched row, with a full, auditable trail your engineers and your stakeholders can both trust.
- Executive summaries and engineering detail in one report
- Drill down to the exact row that differs
- Auditable, exportable evidence trail
Data readiness score
A weighted readiness score grades each dataset on structure, profiling and relationships, turning "is this data any good?" into a clear, defensible number you can act on before feeding it to a model or a migration.
- Weighted score across structure, profiling and relationships
- Spot gaps before they reach a model or migration
- Track readiness as your data improves
One platform,
three planes.
DataGinii is a single pane of glass over a purpose-built data-validation stack. You never touch the machinery underneath.
Experience
The DataGinii workspace: catalog, DQ Studio, jobs and reporting.
Control
Orchestration and metadata: projects, connections, plans and runs.
Data
The engine room: reconciliation runs in your environment, close to your data.
Connects to
your whole stack.
Validate wherever your data lives, cloud or on-prem, across the modern data stack.
See DataGinii on your data.
Join the private beta. Tell us about your migration or pipeline, and we’ll show you how DataGinii proves your data moved correctly.
Get early access