DataSentry

Find bad data before your users do.
Automatic data-quality discovery, evidence-backed explanations, and safe reversible repair.
Local-first. Deterministic by default. AI optional.

Live demo · 30-second start · Use cases · FAQ · PyPI · Examples · MCP setup · Contribute

Release PyPI License

DataSentry scans dirty data, explains evidence, and starts a safe repair loop

中文导读：DataSentry 会先自动发现数据质量问题，再给出样本、比例、置信度等证据。修复采用 propose → preview → apply to a copy → verify → rollback 的保守流程。检测与评分不依赖 LLM，AI 只作为可选辅助，数据可以完全留在本机。

Try it in 30 seconds

Install the current PyPI release and run the zero-config product tour:

Terminal

pip install --upgrade datasentry-ai
datasentry demo

datasentry-demo is also available as a direct console alias.

The demo generates synthetic dirty data, runs the built-in detectors, exports JSON + HTML reports, applies one safe repair to a copy, re-scans the repaired copy, and prints a rollback command. It needs no dataset, cloud service, API key, or LLM.

text

synthetic dirty CSV
        ↓
39 deterministic detectors
        ↓
evidence-backed issues + quality score
        ↓
preview → repaired copy
        ↓
re-scan → verify new/persistent issues

Prefer scanning your own data immediately?

Terminal

curl -L https://raw.githubusercontent.com/Jackxiaozhiren/datasentry/main/demo-data/orders.csv -o orders.csv
datasentry scan orders.csv
datasentry issues list --severity high

Or launch the interactive interfaces:

bash

datasentry          # terminal UI
datasentry-server   # Web UI + REST API at http://localhost:8000/ui/

Start with your problem

If you need to...	Start here
Find suspicious data before you know every rule	`datasentry scan data.csv`
See the full discovery → repair → verify loop	`datasentry demo`
Block severe data issues in CI	`docs/GITHUB_ACTIONS.md`
Add quality gates to dbt / Airflow	`examples/integrations/`
Inspect and repair issues without overwriting the source	`Safe repair`
Give AI agents deterministic data-quality tools	`docs/MCP.md`

The default path is intentionally low-commitment: scan first, inspect evidence, then decide whether a finding deserves a permanent rule or a repair.

Why DataSentry exists

Most data-quality tools are excellent once you already know the expectations, checks, or contracts you want to enforce. Real incidents often start one step earlier: you do not yet know what is wrong.

DataSentry is built around the complete remediation loop:

text

Find → Explain → Fix safely → Verify

Find — discover common quality problems without writing every rule first.
Explain — attach samples, affected counts/ratios, detector evidence, and confidence.
Fix safely — preview changes and apply repairs to a copy instead of mutating the source.
Verify — re-scan the repaired copy and surface persistent or newly introduced issues.

DataSentry fits best when you are looking at data you do not fully trust yet, want evidence before codifying checks, or need a conservative remediation path. It complements expectation- and contract-driven tools rather than requiring you to replace them.

What it catches automatically

DataSentry ships with 39 deterministic detectors covering common failure modes such as:

missing and placeholder values;
invalid emails, URLs, dates, and encodings;
duplicate identifiers and uniqueness violations;
inconsistent categories and cross-field contradictions;
foreign-key and referential-integrity problems;
numeric and statistical outliers;
schema, row-count, score, and issue-distribution drift.

Every scan produces an evidence-backed issue list and a six-dimension quality score across completeness, validity, uniqueness, consistency, integrity, and timeliness.

Safe repair, not blind mutation

bash

# inspect the highest-severity findings
datasentry issues list --severity high

# propose a repair without changing data
datasentry repair propose <issue_id> --file orders.csv

# preview the exact effect
datasentry repair preview <issue_id> --file orders.csv

# apply to a repaired copy; the original is not overwritten
datasentry repair apply <issue_id> --file orders.csv

# re-scan the repaired copy and detect regressions
datasentry repair verify <run_id>

# inspect or undo the repair
datasentry repair diff <run_id>
datasentry repair rollback <run_id>

Repairs are fingerprinted, auditable, and reversible. AI-generated repair proposals remain human-approved state changes.

Quality gates for CI

bash

datasentry scan orders.csv --fail-on high

For GitHub repositories, reuse DataSentry's maintained workflow_call gate instead of duplicating installation and exit-code handling:

yaml

jobs:
  datasentry:
    uses: Jackxiaozhiren/datasentry/.github/workflows/datasentry-quality-gate.yml@main
    with:
      path: data/orders.csv
      fail_on: high

See docs/GITHUB_ACTIONS.md for inputs, artifacts, security boundaries, and version-pinning guidance.

Reports can be exported as JSON, Markdown, HTML, JUnit, and SARIF. The GitHub Actions example fails the workflow on severe findings while still uploading an HTML report for review.

Give AI agents deterministic data-quality tools

DataSentry includes an MCP stdio server:

bash

datasentry mcp --project /path/to/project

MCP-capable clients can scan files, inspect evidence-backed issues, read quality scores and trends, compare drift, validate contracts, manage scheduled jobs, and call DataSentry tools without bypassing the same underlying safety rules used by the CLI and REST API.

Copy-paste setup recipes for VS Code and Claude Desktop are in docs/MCP.md.

Boundary: AI may propose; humans approve state-changing repairs.

Where it fits next to popular data-quality projects

This is a positioning guide, not a winner/loser feature scorecard. These projects solve overlapping but different jobs; check their upstream documentation for current capabilities.

Project	Core mental model	A strong fit when you want...
DataSentry	discover → explain → repair → verify	automatic issue discovery plus a controlled, reversible remediation loop
Great Expectations	Expectations / expressive data tests	explicit validation rules, validation results, and generated data-quality documentation
Soda Core	data contracts and quality checks	YAML contracts and verification across a broad data stack
Deequ	“unit tests for data” on Spark	large-scale data verification in Spark-centric environments
ydata-profiling	one-line profiling / EDA	fast exploratory profiling and shareable analysis reports

DataSentry is intentionally not trying to replace a metadata catalog, lineage platform, or every validator. Its focus is narrower: find bad data, show why it was flagged, and close the repair loop without gambling on the source.

Local-first by design

deterministic detection and scoring run locally;
DuckDB powers core local execution;
OpenAI/Ollama assistance is optional;
PII redaction, encrypted mappings, and LLM audit records are available when AI is enabled;
the original source file is not overwritten by repair workflows.

Data sources

CSV, Parquet, JSONL, XLSX
DuckDB and SQLite
PostgreSQL and MySQL
s3://, gs://, and az:// objects
single files, batches, and globs

History and drift

Persist scans and compare data over time:

bash

datasentry drift latest orders
datasentry score

Tracked signals include schema changes, row-count movement, quality-score changes, and issue-distribution drift.

Architecture

mermaid

flowchart LR
    Sources[Files / DBs / cloud objects] --> DuckDB[Local execution]
    DuckDB --> Detect[39 detectors]
    Detect --> Evidence[Evidence fusion]
    Evidence --> Score[6-dimension score]
    Score --> Reports[Reports / history / gates]
    Reports --> CLI[CLI / TUI]
    Reports --> Web[Web / REST]
    Reports --> MCP[MCP]
    Evidence --> Proposal[Repair proposal]
    Proposal --> Preview[Preview]
    Preview --> Apply[Apply to copy]
    Apply --> Verify[Verify by re-scan]
    Verify --> Rollback[Rollback artifact]
    LLM[Optional OpenAI / Ollama] -. proposes .-> Proposal

Reproducible benchmark

bash

uv sync
uv run python benchmarks/bench_scan.py 1000000 42

The benchmark generates synthetic dirty data and measures profiling, detection/fusion/scoring, numeric-outlier detection, JSONL reading, sampling, score drift, and memory high-water marks. See docs/BENCHMARKS.md.

Development

bash

uv sync
make check          # lint + mypy --strict + tests/coverage
make demo           # exercise the public datasentry demo path
make bench          # benchmark
make build          # distributions

Contributing

Useful contributions include new detectors, connectors, integration examples, reproducible benchmark cases, documentation/translations, minimal bug reproductions, and CLI/TUI/Web usability improvements.

Want a small first contribution? Start with good first issue #14 or good first issue #15.

See CONTRIBUTING.md, CODE_OF_CONDUCT.md, SECURITY.md, and ROADMAP.md. Small contributions are tracked with good first issue and help wanted labels.

Documentation

Project site and live report
docs/FAQ.md — evaluation, safety boundaries, and how DataSentry fits alongside other data-quality tools
examples/ — scenario-first runnable examples
docs/MCP.md — VS Code and Claude Desktop MCP setup
docs/GITHUB_ACTIONS.md — reusable GitHub quality gate
examples/integrations/github-actions/ — copy-paste CI gate
docs/BENCHMARKS.md — benchmark policy
docs/DEVELOPMENT.md — engineering notes
Detect → fix → verify / 中文版

License

Apache-2.0 — see LICENSE.

If DataSentry helps you catch bad data before it reaches production, consider giving the repository a ⭐.
It helps other data engineers discover the project.