Validation
ClariLayer probes your live warehouse to prove metric logic works against real data — before it hits a dashboard or AI agent. This is what separates a context layer from a wiki.
Start FreeCatalogs and wikis document what should be true. But documentation is never in sync with the actual SQL running in the warehouse. It diverges silently.
Upstream schema changes, column renames, and data type shifts break metric logic without warning. No one catches it until the board meeting.
We execute metric logic against your actual data within a bounded test window. Validation evidence is attached to every metric version. No guessing.
Automated checks for null values, uniqueness violations, compilation errors, and data type mismatches. Catches the obvious failures before anyone sees them.
Validation is restricted to a 30-day data window to keep costs low and execution fast. Enough to prove the logic, not enough to break your budget.
Financial-tier metrics cannot be promoted without a passing validation report. Experimental metrics get lighter checks. The rigor matches the stakes.
Every metric version carries its validation report. Auditors, AI agents, and team members can see when it was last proven against real data.
Evidence Surface
A metric can sound right in a meeting and still fail when it meets current schemas, null behavior, or data type changes. ClariLayer records validation evidence alongside the definition so reviewers, auditors, and AI consumers can inspect the proof instead of trusting a stale description.
The evidence is intentionally attached to the metric version, not left in a one-off test log. When a definition is released, later consumers can see which checks ran, what warehouse surface was used, and whether the metric still needs additional review before it can support higher-stakes decisions.
Catalog browse, validation probes, direct deploy, and rollback run through the shared adapter-aware surface for Databricks, Snowflake, and BigQuery connections.
Validation focuses on targeted checks: SQL compilation, source availability, basic integrity assumptions, and bounded data windows. The goal is enough evidence to govern the metric without turning every edit into a warehouse bill surprise.
Observe and query-history ingestion are still Databricks-only today. Validation is broader than Observe: Snowflake and BigQuery share the validation and deploy/rollback paths, but not the live query-history loop yet.
Validation status gives BI users and AI agents a concrete reason to treat one metric differently from another: proven, unverified, stale, or blocked from promotion until the evidence is refreshed.
Validation becomes most powerful when it is paired with approval workflows that match metric risk and the Metric Registry that exposes current trust signals.
Validates metric logic against your actual Databricks, Snowflake, or BigQuery data. Not assumptions, not documentation — real execution against real tables.
Probes for null values, uniqueness violations, compilation errors, and data type mismatches. Catches problems before they reach production.
Experimental metrics can ship with lighter checks. Financial-tier metrics cannot be promoted without a passing validation report. Different rigor for different stakes.
The Switzerland of metric governance. Works across Databricks, Snowflake, and BigQuery without locking you into a single vendor’s ecosystem.
Join the companies building a trusted context layer for their AI agents and business teams.
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