DataGuard AI

Open-source, AI-assisted data quality and governance for modern data platforms.

Scan your data. Find quality and governance risks. Understand why they matter. Generate the fix.

DataGuard AI is a developer-first toolkit for data quality, data governance, PII discovery, schema drift, data contracts, and CI/CD-friendly validation. Detection is deterministic and does not require an LLM. Optional AI assistance can explain structured findings and suggest remediation without making quality detection dependent on a model.

Current version: v0.2.0 public beta.

Why DataGuard AI?

Data teams often manage quality rules, contracts, PII checks, schema drift, metadata, and AI assistants in separate workflows. DataGuard AI provides a lightweight layer developers can run locally or in CI to surface these risks through one interface.

What v0.2 can do

• Profile CSV and JSON data, with optional Parquet support
• Scan DuckDB and PostgreSQL tables
• Run reusable YAML data-quality rules
• Detect nulls, duplicate rows, uniqueness issues, range violations, and freshness risks
• Identify likely PII using value and column-name signals
• Compute transparent quality and governance scores
• Generate standalone HTML and machine-readable JSON reports
• Snapshot schemas and detect schema drift
• Generate starter dbt tests and YAML data contracts
• Export portable Great Expectations expectation configuration
• Run in GitHub Actions
• Explain findings locally, with optional AI-assisted remediation guidance

Quick start

Install from source during the public beta

Until DataGuard AI is published to PyPI, clone the repository and install it locally:

git clone https://github.com/adiranjan25/dataguard-ai.git
cd dataguard-ai
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e .

Run the synthetic retail demo:

dataguard demo --rows 100

Or scan your own dataset:

dataguard scan data/customers.csv

Generate JSON and HTML reports:

dataguard scan data/customers.csv --json-out report.json --html-out report.html

Coming next: after the PyPI release, installation will become simply pip install dataguard-ai.

Detection philosophy

AI assists; deterministic and statistical checks detect and verify.

The default scanning path does not require an LLM. This keeps findings reproducible and allows teams to use DataGuard AI without sending raw production datasets to an external model.

YAML rule engine

Supported v0.2 custom rule types:

• not_null
• unique
• accepted_values
• between
• regex
• max_null_pct
• row_count_between

Example:

quality:
  max_null_pct: 5
governance:
  owner: data-platform@example.com
rules:
  - type: not_null
    column: customer_id
    severity: CRITICAL
  - type: unique
    column: customer_id
  - type: accepted_values
    column: state
    values: [TX, CA, NY]
  - type: between
    column: amount
    min: 0
    max: 100000

dataguard scan customers.csv --config dataguard.yml --html-out report.html

Database scanning

DuckDB

python -m pip install -e ".[duckdb]"
dataguard scan-duckdb analytics.duckdb --table customers --html-out report.html

PostgreSQL

python -m pip install -e ".[postgres]"
export DATAGUARD_POSTGRES_URL='postgresql+psycopg://user:password@host/database'
dataguard scan-postgres --table public.customers --html-out report.html

Current limitation: database scans load the selected table/result into memory. Warehouse-scale pushdown profiling is a roadmap item.

Reports

dataguard scan customers.csv --html-out report.html
dataguard scan customers.csv --json-out report.json

The HTML report includes quality/governance scores, findings, severity, affected columns, suggested remediation, column profiles, and detected PII.

Data contracts and dbt

dataguard contract data/customers.csv --out contract.yml
dataguard generate-dbt data/customers.csv --out schema.yml

These outputs are intended as reviewable starting points rather than replacements for domain-specific contract design.

Great Expectations

dataguard generate-gx data/customers.csv --out gx-expectations.json

The exporter deliberately produces reviewable configuration rather than modifying an existing Great Expectations project.

Schema drift

dataguard snapshot data/customers.csv --out baseline.json
dataguard drift data/customers_v2.csv --baseline baseline.json

GitHub Actions

The repository includes an example workflow at .github/workflows/dataguard.yml that demonstrates scanning sample data and uploading JSON/HTML reports as workflow artifacts.

Project CI separately runs linting and automated tests against Python 3.10, 3.11, and 3.12.

Optional AI explanations

Local deterministic explanations are available without an external model.

python -m pip install -e ".[ai]"
export OPENAI_API_KEY=...
dataguard explain report.json --provider openai

The included provider sends structured findings rather than raw dataset rows. Always review your organization's security, privacy, and data-handling requirements before enabling an external provider.

Architecture

                    Data sources
                         |
       +-----------------+-----------------+
       |                 |                 |
   CSV / JSON         DuckDB          PostgreSQL
   / Parquet             |                 |
       +-----------------+-----------------+
                         |
                         v
                  DataGuard scanner
                         |
        +----------------+----------------+
        |                |                |
     Profiling       Rule engine     PII detection
        |                |                |
        +----------------+----------------+
                         |
                         v
              Quality + governance
                         |
       +-----------+-----+------+-----------+
       |           |            |           |
       v           v            v           v
      CLI         JSON         HTML    Contracts / dbt / GX

Detection and optional AI explanation are intentionally separated.

Commands

Command	Purpose
dataguard scan PATH	Profile file data and report quality/governance findings
dataguard demo	Generate and scan synthetic retail datasets
dataguard scan-duckdb DATABASE --table TABLE	Scan a DuckDB table
dataguard scan-postgres --table TABLE	Scan a PostgreSQL table
dataguard contract PATH	Generate a starter data contract
dataguard generate-dbt PATH	Generate starter dbt tests
dataguard generate-gx PATH	Generate portable GX expectation configuration
dataguard snapshot PATH	Save a schema/profile baseline
dataguard drift PATH --baseline FILE	Compare current schema with a baseline
dataguard explain REPORT.json	Explain findings locally or with optional AI

Run dataguard --help for current CLI options.

Python API

from dataguard.scanner import scan_path

report = scan_path("customers.csv")
print(report.quality_score)

for finding in report.findings:
    print(finding.severity, finding.column, finding.message)

Retail demo

The bundled synthetic retail demo creates customer, order, and inventory datasets with intentionally injected quality/governance problems so developers can explore DataGuard AI without providing proprietary data.

dataguard demo --rows 5000

Project status

DataGuard AI is currently a public beta (v0.2.0). The API, configuration schema, scoring model, and command behavior may evolve before v1.0.

The project is suitable for experimentation, development workflows, demos, and community feedback. Evaluate it against your own requirements before using it as a production control.

Roadmap

v0.3 — Metadata and context

• OpenMetadata integration
• DataHub integration
• dbt artifact ingestion
• Dataset-to-dataset referential checks
• Richer statistical drift and anomaly detection

v0.4 — Agent access

• MCP server
• Agent-accessible quality, contract, and governance tools
• Ollama/local-model provider
• Governance context for AI agents
• Assisted remediation workflows

Toward v1.0

• PyPI distribution and automated release workflow
• Warehouse-scale profiling/pushdown
• Broader integration tests
• Benchmark datasets and reproducible evaluation
• Stable configuration and CLI contracts

Contributing

Contributions are welcome. See CONTRIBUTING.md for development setup and contribution guidance.

Useful first contributions include additional PII detectors, report/export formats, documentation improvements, tests, database adapters, and integration examples.

Security and privacy

• Never submit secrets, credentials, or proprietary production datasets in GitHub issues.
• Use environment variables or an appropriate secrets manager for database and model credentials.
• Treat detected PII findings as sensitive operational metadata.
• Review organizational security/privacy requirements before using an external AI provider.
• See SECURITY.md for vulnerability-reporting guidance.

License

DataGuard AI is licensed under the Apache License 2.0. See LICENSE.