What it is

Elementary Data is an open-source, dbt-native data observability tool for data and analytics engineers. It lives in the dbt and modern data warehouse ecosystem, and it is distributed as a CLI that works alongside the Elementary dbt package. The project is licensed under Apache-2.0, is written primarily in HTML, and is maintained by the Elementary team with a homepage at elementary-data.com and documentation at docs.elementary-data.com.

The concrete problem it solves is visibility into data pipelines that have already been built and tested. The CLI connects to a warehouse and reads the metadata, artifacts, and test results that the Elementary dbt package collects during dbt runs. From that material it generates a data observability report, surfaces anomalies and failed tests, sends alerts to Slack and Microsoft Teams, and tracks model and test performance trends over time. Instead of assembling a separate monitoring stack, teams get observability output derived from the dbt code and runs they already have.

Key capabilities

• Anomaly detection tests that collect data quality metrics and detect anomalies as native dbt tests.
• End-to-end data lineage enriched with the latest test results, for impact and root cause analysis of data issues.
• A data quality dashboard that serves as a single interface for monitoring and test results.
• Models performance monitoring that tracks model and job run results and performance over time.
• Configuration-as-code, with Elementary configuration managed inside the dbt code.
• Alerts with actionable messages, custom channels, and tagging of owners.
• A dbt artifacts uploader that saves metadata and run results as part of dbt runs.

Who uses it and how

• Analytics engineers who want data quality checks expressed as dbt tests rather than in a separate tool.
• Data teams that need failed tests and anomalies routed to Slack or Microsoft Teams with owner tagging.
• Teams performing root cause and impact analysis by combining lineage with the latest test results.
• Organizations monitoring model and job performance trends across repeated dbt runs.
• Teams that want a data catalog view of dataset descriptions, columns, and dataset health.

Getting started

The README points to the quickstart guide at docs.elementary-data.com/oss/quickstart, which covers installing and configuring the Elementary dbt package together with the CLI. The README does not name a Docker image, package manager, or other distribution channel.

When to use it — and when not to

Elementary OSS is the right fit when the goal is a dbt-native observability report with Slack and Teams alerts, and when configuration-as-code inside dbt is acceptable. Teams that need automated ML monitoring