Introduction
Elementary is an open-source data monitoring and observability tool designed for dbt (Data Build Tool) users. It helps data teams detect anomalies, track data quality metrics, and gain insights into the health of their dbt transformations. The tool is particularly useful for organizations leveraging dbt for ETL pipelines, ensuring data consistency and reliability.
Features & Use Cases
Elementary provides key capabilities that enhance data observability in dbt workflows:
- Automated Data Monitoring – Tracks data changes, missing records, schema modifications, and other anomalies.
- Alerting & Notifications – Integrates with Slack, email, and other platforms to notify teams about potential data issues.
- Query Performance Insights – Monitors dbt model execution times and provides recommendations for optimization.
- Historical Data Trends – Enables users to visualize trends in data quality over time, aiding in root cause analysis.
- Lightweight Deployment – Can be installed quickly as a dbt package without complex infrastructure requirements.
Use cases include:
- Ensuring ETL pipelines produce accurate and timely data.
- Monitoring business-critical datasets for unexpected changes.
- Reducing the manual workload for data engineers by automating data health checks.
Pros & Cons
Pros
✔ Seamless integration with dbt without additional dependencies.
✔ Automated alerts reduce manual effort in identifying data issues.
✔ Helps improve data trust and reliability across teams.
✔ Open-source and actively maintained with community contributions.
Cons
✖ Limited functionality outside dbt ecosystems.
✖ May require tuning to reduce false positives in anomaly detection.
✖ Initial setup requires defining relevant metrics for effective monitoring.
Integration & Usability
Elementary is designed for easy integration with dbt projects. Users install it as a dbt package and configure it using YAML-based settings. It works with common data warehouses like Snowflake, BigQuery, and Redshift, making it accessible to a broad range of dbt users. The command-line interface and dashboard provide an intuitive experience for both data engineers and analysts.
Final Thoughts
Elementary is a valuable tool for organizations relying on dbt for data transformation, offering real-time monitoring and observability features that enhance data reliability. Teams handling critical ETL processes can benefit from its automated anomaly detection and alerting system. While its scope is limited to dbt users, its lightweight and efficient design make it a strong addition to modern data workflows.
Last Releases
- v0.19.0What’s Changed Enable support for multiple links and icons in alert messages by @MikaKerman in #1927 using elementary 0.19.0 by @arbiv in #1942 Known Issues dbt-databricks must be <1.10.2 by… Read more: v0.19.0
- v0.18.1What’s Changed Athena now works in the CLI by @GuyEshdat in #1870 Allow contributor PRs to run tests by @haritamar in #1866 Add NOT_CONTAINS filter type by @MikaKerman in #1876… Read more: v0.18.1
- v0.17.0What’s Changed Alerts Table Block: Introduced a new Table Block feature to present test results within alerts, enhancing data readability and user comprehension. Selectable Alert Sections: Extended the support… Read more: v0.17.0