Glossary

Term	Meaning
Datasource	A store DataQ runs checks against: Snowflake, ADLS Gen2, S3, Unity Catalog, Apache Iceberg.
Orchestration provider	A workflow engine DataQ observes + triggers from: ADF, Airflow, dbt. Not a datasource.
Connection	Stored credentials + config for one datasource or orchestration provider.
Suite	A named set of checks that runs against one connection's target.
Check	One data-quality rule — in v1, a Great Expectations expectation (or a freshness/volume monitor).
Expectation	A Great Expectations assertion about data (e.g. "column not null").
Monitor	A non-expectation check kind: freshness (staleness) or volume (row-count range).
Run	One execution of a suite. Lifecycle status: queued → running → succeeded/failed/cancelled.
Result	One check's outcome in a run — status pass / warn / fail / critical (or skip / error).
Severity tier	warn < fail < critical, derived from thresholds banding the unexpected-%.
Health score	Severity-weighted, SQL-normalised score (0–100) summarising workspace quality.
Trigger binding	A mapping (provider, pipeline/DAG, env) → suite that runs a suite when a pipeline succeeds.
pipeline_runs vs runs	Orchestration runs vs DataQ check runs — linked, never conflated.
Secret store	Where credentials live — Azure Key Vault (prod) or env/redis (dev), behind one seam.
MCP	Model Context Protocol — DataQ exposes 8 curated tools at /mcp for AI assistants.
ADR	Architecture Decision Record — docs/adr/, one markdown per significant decision.

Contact / ownership

Maintainer & docs owner: @TheurgicDuke771. File issues / PRs on GitHub.