Cognite Data Quality
cognite-data-quality is a Python package for SHACL-based data quality validation in Cognite Data Fusion (CDF). It validates DMS instances and time series data against declarative rules, posts results to the CDF Records API, and provides tools to run validation locally or deploy it with Cognite Functions and Workflows.
Industrial data must meet quality standards before it can be trusted for analytics, dashboards, or automation. This package automates those checks and records pass/fail outcomes so teams can monitor, triage, and fix issues at scale.
Validation journey
The Usage documentation follows one end-to-end path from baseline validation to advanced conditional flows:
- Single instance data quality validation
- Graph consistency
- Edge existence
- Uniqueness
- Time series
- Conditional logic
- Chained conditional logic
See the Usage guide for context, supporting operations, and links into each mode.
Next steps
- Installation — install the package and set up your environment
- Get started with data quality validation — run your first local validation in about five minutes
- Confirm that rules work — local sample vs full estate historic
- Changelog — release notes