DataRanger
DataRanger patrols your data, fixes what it can prove, and calls you for the rest.
A park ranger patrols the trails, fixes what is safe to fix on the spot, calls in anything dangerous, and files a report. DataRanger does the same for your tables.
Most data-quality tools stop at an alert. Letting an AI agent fix data is the obvious next step, and a risky one: a wrong fix can pass every monitor you have. DataRanger only applies a fix after it has been proven against source data, and it hands everything else to a person with the evidence attached.
The patrol loop
- Detect. Rules and statistics find nulls, duplicates, schema drift, late partitions, broken references, unit errors and silent shifts. No LLM.
- Diagnose. A language model answers one narrow question: what is the likely cause?
- Propose fix. A templated fix (dedupe, backfill, quarantine, patch). The model fills in parameters only.
- Verify. pipeverify checks the fix against source data, on a copy.
- Gate. Proven and low risk: applied as a new version, never an overwrite. Anything else: escalated.
- Report. Every detection, fix and decision lands on a live dashboard and an audit log.
Status
Early. Version 0.0.1 reserves the name and ships the command-line skeleton:
pip install dataranger
dataranger plan
The first working patrol loop (DuckDB, open-weights models through Ollama, static dashboard) is planned for 0.1.0, alongside the DQ-Repair benchmark.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for dataranger 0.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
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| File | Interpreter | ABI | Platform | Actions Reset |
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| dataranger-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for dataranger-0.0.1-py3-none-any.whl |
Total release size: 7.5 kB
Release files / dataranger-0.0.1.tar.gz
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