Why it exists

The job passed. The dashboard is still wrong.

Most analytics reliability issues do not begin with a dramatic pipeline crash. They begin quietly: a source table arrives half-full, a join filters out valid records, a schema changes upstream, or a stale model keeps flowing into executive dashboards.

Relium is built for those quiet failures. It looks at metadata, SQL structure, and lineage so teams can catch anomalies before the first confused stakeholder asks why the numbers changed.

The goal is not to replace dbt. The goal is to add an evidence layer around dbt and SQL pipelines: what changed, what might be broken, which downstream models are exposed, and what should be checked first.

The current MVP was tested on a 511,000-row synthetic e-commerce warehouse with source tables, dbt-style models, multi-hop lineage, SQL risks, Slack alerts, and incident reports.