Schema validation
Structure, type, and constraint checks at ingestion. Deviations are classified, notified, and optionally routed to a recovery queue.
En clair : We reject the malformed before it pollutes the whole pipeline.
In plain terms
Quality is decided at the intake.
Data are checked on arrival: schema validation, anomaly detection, deduplication, timestamps, historization, catalog, metadata, and lineage tracking. Each rule is versioned and tied to a threshold, an alert, and a recovery action.
We distinguish blocking errors, warnings, and slow drift. The goal is not to reject everything, but to make visible what threatens decision-making or automation.
Lineage links source, transformation and usage: you know where an indicator comes from, who changed it, and what impact a schema change has. That is the basis of a credible audit and shared trust between business and technology.
Governance aligns with your existing tools (catalog, data quality, SIEM) or is built progressively. We avoid heavy theoretical frameworks that slow teams without producing evidence.
RulesVersioned controls
LineageEnd-to-end traceability
FreshnessMeasured freshness
RecoveryReplayable flows
What we do
Harden what feeds your decisions.
Concrete controls so data stays usable over time.
Data quality
Indicators, thresholds and alerts on stream compliance: completeness, uniqueness, freshness, business consistency. Dashboards show the real state, not an intention.
En clair : Quality becomes an operational KPI, not an annual report.
Data lineage
End-to-end traceability: source → processing → usage. Useful for audit, debugging and impact analysis during changes.
En clair : We know where every figure comes from and where it goes.
Catalog & metadata
Living documentation of datasets, owners, sensitivity and business meaning. The catalog syncs with pipelines to stay current.
En clair : Everyone finds the right data, with the right level of confidence.
Audit & compliance
Access evidence, historization, and regulatory controls. We produce usable trails for internal or external audits.
En clair : Compliance rests on evidence, not slides.
Data observability
Monitoring of pipelines, errors, latencies, and quality drift. Runbooks describe how to respond without improvising.
En clair : When it breaks, we know what to look at and what to replay.
Frequently asked questions
Quality & governance — details.
Does governance slow down projects?
Well calibrated, it accelerates: less manual rework, fewer incidents, more confidence in decisions. We start with high business-impact rules, then expand.
Do you work with an existing catalog?
Yes. We connect to your tools or propose a progressive approach if the catalog is missing. What matters is staying in sync with the reality of the pipelines.
What to do with rejected data?
Dead-letter, quarantine, and correction workflows by criticality. Nothing is silently discarded without traceability.
How do you define thresholds?
With the business: thresholds that are too strict block, too loose let noise through. We iterate on real datasets.
Is quality compatible with real time?
Yes. Lightweight controls in streaming, heavier controls in consolidation batches, depending on acceptable latency.
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