All 30 Dependencies
Every package Elementary depends on, ranked by repo health score.
Elementary is the open-source CLI half of a dbt-native data observability stack: it reads the metadata, test results, and run artifacts that the companion Elementary dbt package writes into your warehouse, then turns that raw telemetry into anomaly alerts, a data quality dashboard, and lineage graphs — without shipping your data to a third-party service. Built by Elementary, a company backed by Y Combinator (W23), the project has grown into one of the most widely adopted tools in the dbt ecosystem, with over 2,300 GitHub stars and dozens of releases shipped every year.
Under the hood, the `edr` CLI (a Click-based entry point defined in `elementary/cli/cli.py`) wraps a dbt runner abstraction that can invoke dbt via subprocess, the dbt Fusion engine, or a programmatic API, letting it slot into almost any existing dbt project without changing how teams already run dbt. Anomaly detection, freshness checks, and volume/schema monitors are implemented as native dbt tests and macros in the sibling dbt-data-reliability package, so quality rules live in version-controlled dbt code instead of a separate rules engine; the CLI then fetches those results, builds a static HTML report, and routes failures to Slack or Microsoft Teams through dedicated alert integrations.
Elementary intentionally keeps the CLI and dbt package fully open under Apache-2.0 — there is no license check, feature flag, or paywall anywhere in this repository — while offering a separate hosted product, Elementary Cloud, for teams that want managed ML-based anomaly detection, column-level lineage from ingestion to BI, and AI agents layered on the same open data model. That makes it a rare data-observability tool where the self-hosted path is a genuine first-class product rather than a crippled trial of the commercial one.
