elementary-data

What it is and what it does

Elementary OSS is a command-line tool that integrates with dbt to provide data observability across your warehouse. It works alongside the Elementary dbt package to collect metadata, test results, and performance metrics from your dbt runs, then synthesizes them into a unified observability report. The tool can detect anomalies in data quality, surface failed tests, track model performance trends, and send actionable alerts to Slack or Microsoft Teams.

The package depends on dbt-core and a range of cloud storage and notification libraries (boto3, azure-storage-blob, google-cloud-storage, slack-sdk, pymsteams) to connect to your warehouse and communicate findings. It's designed for analytics engineering teams that want to monitor data reliability without building custom observability infrastructure, though the OSS version is more limited than the commercial Elementary Cloud offering.

Use it for:

• Monitor dbt test results in real time and alert teams to failures via Slack or Teams
• Detect anomalies in data freshness, volume, and schema changes across your warehouse
• Track performance trends of dbt models and jobs over time to identify regressions
• Explore data lineage and test results together to diagnose root causes of data issues
• Generate a centralized data quality dashboard for non-technical stakeholders

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Elementary OSS is a dbt-native CLI for data observability that connects to your warehouse, reads dbt metadata and test results, and generates observability reports with anomaly detection and alerts to Slack and Microsoft Teams.

Yes, if you use dbt and need observability without building custom tooling. Elementary OSS is actively maintained, has no known vulnerabilities, and offers genuine value for teams monitoring data quality. The substantial dependency footprint is justified by its integration with multiple cloud platforms and notification channels. Consider it especially if you already use Slack or Teams for team communication and want dbt-native monitoring.

Install

elementary-data on PyPI

pip install elementary-data

uv add elementary-data

poetry add elementary-data

Installing elementary-data

Before you install

Low install friction with a pure Python wheel. Active maintenance with a recent release 37 days ago and ongoing commits. Depends on 22 runtime packages including dbt-core, cloud storage clients, and notification SDKs—a substantial but standard dependency footprint for a data platform tool.

License in practice

Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most organizational contexts.

Quickstart

pip install elementary-data

from elementary import run_elementary_report
# Requires dbt project with Elementary dbt package installed and warehouse connection configured

Requires dbt-core installed, an active dbt project with the Elementary dbt package, and a configured warehouse connection (Snowflake, BigQuery, Redshift, etc.).

Verify before relying

• Specific warehouse platforms supported beyond those mentioned (Snowflake, BigQuery, Redshift) in keywords
• Whether anomaly detection thresholds are configurable or use fixed defaults
• Performance characteristics when monitoring large numbers of models or tests
• Exact scope of 'column-level lineage' available in OSS vs. Elementary Cloud

Package facts

Evidence: elementary_data-0.25.1-py3-none-any.whl

Keywords: data, lineage, data lineage, data warehouse, DWH, observability, data monitoring, data observability, Snowflake, BigQuery, Redshift, data reliability, analytics engineering

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