Elementary
dbt-native data observability and anomaly detection
Elementary is profiled here as a Data Quality tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Elementary is an open-source data observability tool built for teams running dbt, created by the company of the same name founded by Maayan Salom and Or Avidov. It installs as a dbt package, collects metadata and test results from every run, and adds anomaly-detection monitors expressed as dbt tests, so observability lives inside the existing transformation workflow. From the collected results it generates a shareable report and sends Slack or Teams alerts when a test fails or a metric drifts from its expected range. Elementary Cloud adds managed alerting, column-level lineage, and dashboards on top of the open-source package.
Key Capabilities:
Installation as a dbt package inside existing projects
Anomaly-detection monitors written as dbt tests
Collection of test results and run metadata into tables
Automated alerts to Slack and other channels
Column-level lineage and a data catalog in the cloud version
MIT-licensed package with a managed Elementary Cloud
Alternative tools
- Datafold
Data diffing and regression testing for data teams
- Anomalo
Automated data quality monitoring with machine learning
- Soda
Data quality testing defined in a readable check language
- GX (Great Expectations)
Declarative data quality testing for pipelines