Data quality, observability, semantic, and causality library
Project description
dqtlib
Open-source data quality, lineage, semantic layer & causality — for dbt, warehouses and data lakes.
pip-installable Python library for watching dbt-built warehouses and any SQL warehouse for statistical drift, anomalies, silent regressions, and explaining why metrics moved.
pip install dqtlib
The import name is dqt:
from dqt import Check, Runner, MemoryStore
Full documentation and examples: https://github.com/antonbarr-data/dqt
Detector documentation
64 statistical detectors across 10 groups — drift, outliers, time series, distribution, information theory, pattern, referential, schema, basic, and custom.
Every detector has a structured page at docs/algorithms/<group>/<slug>.md covering:
- What it computes and its parameters
- When it works well and when it fails (with concrete failure-mode table)
- Default-threshold calibration — empirical FPR across six canonical data shapes (Normal, Lognormal, Poisson, Beta, Pareto, Exponential)
- Recommended thresholds per data shape
- Canonical citation and runnable Python API example
Browse the full catalog: docs/algorithms/README.md
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