Apiome Data Quality
A schema-aware quality gate for every load — reject, quarantine, or cleanse bad rows before they land.
Apiome Data Quality enforces DQ rules during the load process, wired directly into apiome Class and Property definitions. Rows that fail validation are routed to reject, quarantine, or cleanse modes, and rules travel with schema versions — flagged automatically when a Class changes major version. Author rules with a live validation stream and gate any Forge pipeline on the pass rate.
What ships with Data Quality
Every Data Quality surface is wired into the rest of the Apiome platform — no glue code, no separate identity, no bolt-on integrations.
Live rule builder
Author DQ rules and watch a real-time validation stream classify rows as passed, rejected, quarantined, or cleansed as you edit.
Reject / quarantine / cleanse routing
Send failing rows to reject, hold them in quarantine for review, or auto-fix them with cleanse policies during load.
Versioned rule sets
Group rules per class with versioning and inheritance; rules travel with schema versions and flag on major-version changes.
Cleanse policies
Apply auto-fix rules — trim, normalize, fill-default, and regex-replace — to repair rows inline instead of rejecting them.
Quality scoring & SLAs
Track pass/fail trends, per-class quality scores, and SLA thresholds, with breach notifications and escalation.
Pipeline gates
Embed DQ gates in Forge pipelines and load jobs so runs halt when the pass rate falls below the configured threshold.
A look inside Apiome Data Quality
Live design previews from the Data Quality mockup pack — 8 surfaces in total.
Review failed rows in quarantine and release, re-validate, or discard them — with live counters for processed, passed, rejected, quarantined, and cleansed.
Per-run validation results — rows processed, passed, rejected, and quarantined with pass-rate bars for every class.
Configure DQ gates inside Forge pipelines and load jobs so a run only proceeds when the pass rate clears its threshold.
Use cases
Data Quality is designed around the way real teams actually work — not the way a tool wants them to work.
Gate a nightly load so it aborts when the pass rate drops below 95% instead of shipping dirty data downstream.
Work the quarantine queue — release, re-validate, or discard 1,284 held rows — instead of re-running whole loads.
Attach a versioned rule set to a Class so every future load inherits the same validation automatically.
- Schema-versioned rule sets with inheritance and change flagging
- Pass-rate SLA thresholds with breach notifications and escalation
- Full run-result history: pass / fail / quarantine breakdown per load
- Forge pipeline gate enforcement backed by load_jobs
- Auditable quarantine actions with reviewer attribution
- Draft DQ rules from Class and Property definitions
- Explain why a specific row was rejected or quarantined
- Recommend cleanse policies to auto-fix common failures
- Suggest SLA thresholds from historical pass-rate trends
AI rule author and triage assistant
Data Quality uses AI to keep rules current and the quarantine queue moving — suggesting validations from your schema and explaining exactly why each row failed.
Every Data QualityAI feature is grounded in your tenant's data, runs under your data-residency policy, and respects every role and ACL the platform enforces.
Every surface in Apiome Data Quality
A look at the 8 screens designed for this suite — covering everything from day-1 onboarding to day-100 operations.
