Overview:
Elementary OSS is an open-source CLI that provides dbt-native data observability. It works with the Elementary dbt package to read warehouse metadata, artifacts, and test results, enabling teams to generate a data observability report and receive alerts. It is designed for data teams already using dbt who need to monitor data quality, track model performance, and detect anomalies without leaving their dbt workflow.
Core Features:
Anomaly detection tests: Collect data quality metrics and detect anomalies as native dbt tests.
Automated monitors: Out-of-the-box cloud monitors for freshness, volume, and schema changes.
End-to-end data lineage: Enriched with test results for impact and root cause analysis.
Data quality dashboard: A single interface for monitoring data and test results.
Alerts: Actionable alerts to Slack and Microsoft Teams with custom channels and owner tagging.
AI-powered data tests: Validate and monitor both structured and unstructured data using AI-powered tests.
Use Cases:
dbt users who want to monitor data quality and detect anomalies as part of their existing dbt pipelines.
Data engineers who need to track model and job performance trends over time.
Teams that want to receive alerts on failed tests and data freshness issues in Slack or Microsoft Teams.
Data teams investigating the root cause of issues using enriched end-to-end lineage.
Why It Matters:
Elementary OSS is one of the few open-source observability tools built natively for dbt, allowing teams to add monitoring and alerting directly into their dbt project. Its configuration-as-code approach means observability is managed with the same conventions as dbt code. The project provides a data quality dashboard and lineage view without requiring a separate platform, and its alerting capabilities support custom channels and owner tagging for actionable incident response.