Elementary OSS: dbt-native data observability
Built by the Elementary team, helping you deliver trusted data in the AI era.
Elementary OSS is the open-source CLI for dbt-native data observability. It works with the Elementary dbt package to generate the basic Elementary observability report and send alerts to Slack and Microsoft Teams.
For teams that need data reliability at scale, we offer Elementary Cloud, a full Data & AI Control Plane with automated ML monitoring, column-level lineage from source to BI, a built-in catalog, and AI agents that scale reliability workflows for both engineers and business users.
How It Works
Elementary OSS connects to your warehouse and reads the metadata, artifacts, and test results collected by the Elementary dbt package.
With this information, it can:
- Generate a data observability report
- Surface anomalies and failed tests
- Send alerts to Slack and Teams
- Track model and test performance trends
Quickstart
Follow the quickstart guide to install and configure the Elementary dbt package and CLI:
👉 https://docs.elementary-data.com/oss/quickstart
Features
- Anomaly detection tests - Collect data quality metrics and detect anomalies, as native dbt tests.
- Automated monitors - Out-of-the-box cloud monitors to detect freshness, volume and schema issues.
- End-to-End Data Lineage - Enriched with the latest test results, for impact and root cause analysis of data issues. Elementary Cloud offers Column-Level-Lineage from ingestion to BI.
- Data quality dashboard - Single interface for all your data monitoring and test results.
- Models performance - Monitor models and jobs run results and performance over time.
- Configuration-as-code - Elementary configuration is managed in your dbt code.
- Alerts - Actionable alerts including custom channels and tagging of owners.
- Data catalog - Explore your datasets information - descriptions, columns, datasets health, etc.
- dbt artifacts uploader - Save metadata and run results as part of your dbt runs.
- AI-Powered Data Tests & Unstructured Data Validations - Validate and monitor data using AI powered tests to validate both structured and unstructured data
Support
For additional information and help:
- Join thousands of users in the Slack community (Release announcements, community and AI support, discussions, etc.)
- Open a GitHub issue (Bug reports, feature requests)
- Check out the contributions guide and open issues.
Elementary contributors: ✨
Release files for elementary-data 0.26.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| elementary_data-0.26.0.tar.gz | 1.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| elementary_data-0.26.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.9 MB
Release files / elementary_data-0.26.0.tar.gz
| Download URL | elementary_data-0.26.0.tar.gz |
|---|---|
| Size | 1.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.13
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 10, 2026.
Transparency logRelease files / elementary_data-0.26.0-py3-none-any.whl
| Download URL | elementary_data-0.26.0-py3-none-any.whl |
|---|---|
| Size | 1.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.13
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 10, 2026.
Transparency log
