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DataGuard AI can run repeatable checks on CSV, JSON, optional Parquet, DuckDB, and PostgreSQL inputs inside GitHub Actions, then produce machine-readable JSON and human-readable HTML reports. The important implementation detail is that scanning and enforcement are different operations: a scan can complete successfully even when it finds nulls, duplicates, drift, or other violations. To block a pull request or deployment, add a second step that reads the JSON report and applies thresholds your team has defined.
DataGuard AI is listed on PyPI as public-beta version 0.2.1, released October 3, 2026. Pin that version (or another version you have explicitly validated), treat its API and configuration as changeable, and validate generated contracts and test files before relying on them.
What does DataGuard AI do in a CI workflow?
DataGuard AI is an open-source Python toolkit for data quality and governance. Its deterministic checks identify problems such as missing values, duplicate rows, uniqueness violations, out-of-range values, freshness risks, configured rule failures, likely personally identifiable information, schema changes, and drift. It can also calculate quality or governance scores, save schema snapshots, and emit JSON and HTML reports.
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The project describes its philosophy as “AI assists; deterministic and statistical checks detect and verify.” In practice, the validation path does not require a language model. Optional AI assistance explains structured findings and suggests remediation; it is a separate capability with its own data-handling implications.
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GitHub Actions is useful here because every pull request, branch push, or deployment can run the same checks in a clean runner. Developers get feedback while changing an ETL job, a contract, or a fixture instead of discovering the problem only after data reaches a shared environment.
How does the scan-versus-policy distinction affect build results?
A successful scan means that the runner executed the scan and generated the requested reports. It does not automatically mean that the data met your quality requirements. The author tutorial states that enforcing a quality gate requires an additional policy step that evaluates the JSON report against agreed thresholds.
Keep the responsibilities separate:
| Stage | Purpose | What success means |
|---|---|---|
| Scan and report | Inspect the selected source and write JSON and, optionally, HTML. | The tool ran and produced reports; findings may still exist. |
| Artifact upload | Preserve reports for pull-request reviewers and later troubleshooting. | GitHub stored the files from the workflow run. |
| Policy evaluation | Interpret findings, severities, scores, or counts according to your contract. | The policy script exits successfully only when the agreed conditions pass. |
This design prevents an important false positive: a green scan step is not evidence that every row is valid. Your policy step must explicitly decide which findings are warnings, which are errors, and which should block a change.
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Start with a small, representative dataset or a test database result. Decide which data contract the check is protecting, where the source will come from in the runner, and which report files reviewers need. Also decide whether optional AI explanations are permitted under your privacy rules.
- Pin the package. Add the DataGuard AI release you have tested to the project’s dependency management. Version 0.2.1 is the public beta identified in the October 3, 2026 PyPI listing; future releases may change APIs, configuration keys, scoring, or command behavior.
- Make the input reproducible. A checked-in fixture, a generated test extract, or a controlled DuckDB/PostgreSQL query result is easier to review than an unversioned external table.
- Define policy owners. Data engineering, analytics, and compliance stakeholders may assign different consequences to the same finding. Record the decision before writing the gate.
- Choose report paths. Use stable JSON and HTML filenames so the upload step and the policy step always read the intended files.
- Control secrets. Database credentials belong in GitHub encrypted secrets or environment-level secret stores, never in a workflow file or a fixture.
How do I run a DataGuard AI scan in GitHub Actions?
Use four conceptual steps in one workflow job. The package’s current documentation should supply the exact invocation and configuration syntax; do not copy a command from an older beta release without checking it against the pinned version.
- Install Python and the pinned package. Select the Python version supported by your project, create the runner environment, and install the exact DataGuard AI version recorded in your dependency file.
- Run the scan. Point the documented DataGuard AI command at the CSV, JSON, optional Parquet file, or selected DuckDB/PostgreSQL source. Configure JSON output for automation and HTML output when a visual report will help reviewers.
- Upload both reports. Add a GitHub Actions artifact-upload step after report creation. Uploading JSON preserves the machine-readable evidence used by the gate; uploading HTML gives a person a convenient summary after the run.
- Evaluate the policy. In a subsequent step, read the JSON file, apply your thresholds, and deliberately set the process exit status. A policy failure, rather than the presence of a finding alone, is what should make the job fail.
Keep the scan and policy steps distinct even if they run in the same job. That separation makes it possible to inspect a report when a policy script has a bug, or to run a non-blocking informational scan for an early adoption period.
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Should I publish JSON, HTML, or both?
Publish both when possible. JSON is the contract between the scanner and automation; HTML is optimized for a human reviewing a failed or surprising run.
| Output | Best use | Trade-off |
|---|---|---|
| JSON | Policy scripts, archival, diffing findings, and integration with other tooling. | Reviewers must understand the report schema or use another viewer. |
| HTML | Pull-request investigation and quick explanation of affected checks. | Not a reliable input for a gate because presentation structure can change. |
Store the reports as workflow artifacts with a retention period that matches your incident and audit needs. If a run handles sensitive data, confirm that artifact access, retention, and repository permissions meet your organization’s requirements.
How do I turn the JSON report into a quality gate?
Write a small policy evaluator that treats the report as input and your data contract as the decision rule. DataGuard AI does not prescribe universal thresholds, so the values must come from your team’s risk tolerance and the dataset’s intended use.
- Inspect the report schema produced by the pinned release. Identify how the report represents checks, severities, counts, percentages, scores, and errors. Do not assume field names from an example generated by another version.
- Classify findings. Decide which conditions are blocking errors, which create warnings, and which are informational. For example, a production customer table might block any unexpected schema change, while a development fixture might only warn on a small freshness deviation.
- Set measurable thresholds. Express rules as counts, percentages, ranges, accepted values, or score floors. Record the unit and scope: a null percentage for one column is different from a null percentage across an entire table.
- Handle tool failures separately from data failures. A missing input, malformed configuration, authentication error, or report-write failure should normally fail the job immediately. A valid report containing findings should be evaluated by the policy rules.
- Return an explicit exit status. The evaluator should exit successfully only when every blocking rule passes. Include a concise failure summary in the Actions log while retaining the full JSON and HTML as artifacts.
- Test the gate with fixtures. Commit one passing fixture and intentionally failing fixtures for nulls, duplicates, range errors, and schema changes that matter to your contract. This verifies the policy logic without relying on a live production table.
Keep threshold definitions under version control. A policy change then receives the same review as a code change, and a historical workflow run can be interpreted using the policy that was active at that time.
Which checks and YAML rules can I configure?
The package documents built-in detection for nulls, duplicate rows, uniqueness, ranges, freshness risks, likely PII, schema snapshots, and drift. Reusable YAML rules include the following types:
| Rule type | What it expresses | Important interpretation |
|---|---|---|
not_null |
A field must contain a value. | Define whether empty strings or whitespace count as missing in your data contract. |
unique |
Values must not repeat. | Apply only where uniqueness is required; a column ending in _id may be a repeating foreign key. |
accepted_values |
Values must come from an allowed set. | Plan how new legitimate values are introduced so the gate does not block intentional domain changes. |
between |
Values must fall within a numeric or comparable range. | Clarify whether endpoints are inclusive and how nulls are treated. |
regex |
Values must match a pattern. | Use a pattern that reflects the actual contract rather than an overly strict guess. |
max_null_pct |
Missing values may not exceed a percentage. | State the denominator and whether the limit applies per partition or to the whole input. |
row_count_between |
Total rows must stay within a lower and upper bound. | Account for legitimate seasonality, incremental loads, and filtered extracts. |
The toolkit can generate starter dbt tests and YAML contracts and export Great Expectations configuration. Treat those outputs as drafts: compare every generated assertion with the domain contract, adjust naming and thresholds, and review them before committing.
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What data sources can the workflow scan?
DataGuard AI supports CSV and JSON, with optional Parquet support, and can scan selected tables or results from DuckDB and PostgreSQL. File-based fixtures are usually the simplest starting point because they make pull-request runs deterministic.
Database scans have a material limitation: selected tables or query results are loaded into runner memory. The package describes warehouse-scale pushdown profiling as a roadmap item, not as a current capability. A large table can therefore create memory, runtime, network, and cost concerns even if the checks themselves are straightforward.
Before pointing a workflow at a shared or production database, evaluate:
- How many rows and columns the runner will load into memory.
- Whether a bounded, representative query can satisfy the development check.
- Whether credentials permit only the reads required by the job.
- Whether report artifacts could expose sensitive values or metadata.
- Whether the workflow’s schedule and concurrency could add meaningful database load.
Are AI explanations required, and what leaves the runner?
No. Core detection is described as deterministic and independent of an LLM, so the same input and configuration can be checked without an external model provider. Optional AI assistance can explain structured findings and suggest remediation, but it is not required to identify the underlying violations.
The package says the external provider receives structured findings rather than raw dataset rows. That distinction reduces exposure of row-level data, but it does not remove the need for a privacy review: structured findings can still contain sensitive column names, values, or business context. Enable the provider only after your security and data-governance teams approve the data flow, retention, and provider terms.
Can I use this workflow with production data?
You can evaluate it against production-like contracts, but the available guidance does not establish production-scale performance or monitoring coverage. The author tutorial recommends assessing scanning cost, data sensitivity, and performance before applying the same pattern to large production datasets, and states that the workflow does not replace production monitoring.
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A safer adoption path is to begin with test data and development extracts, then expand deliberately:
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- Run the scan in a non-blocking mode while measuring runner memory, duration, database load, and artifact sensitivity.
- Introduce blocking rules only after owners agree on thresholds and remediation responsibilities.
- Keep separate operational monitoring for freshness, volume, pipeline health, and incidents that CI cannot observe after deployment.
What commonly goes wrong, and how do I recover?
The job is green even though the report lists violations
That is expected when only the scan step runs. Add a policy evaluator that reads JSON, compares findings with explicit thresholds, and returns a failing exit status for blocking conditions.
The policy script fails because fields are missing
Check the report schema generated by the exact pinned DataGuard AI version. Treat malformed or incomplete reports as tool failures, and add a fixture-based test whenever you upgrade the beta package.
A foreign-key column is flagged as non-unique
Do not infer uniqueness from a suffix such as _id. Remove the uniqueness rule unless the dataset contract requires one distinct value per row, or replace it with the relationship check your model actually needs.
A database scan exhausts runner memory
Reduce the selected result to a representative, bounded development dataset, or move the check to an environment designed for larger profiling workloads. Do not assume that a successful small-table run proves warehouse-scale suitability.
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How should a team maintain the workflow as the beta changes?
Pin the package, review release notes before upgrading, and run the same passing and failing fixtures against the new version. Check command syntax, configuration keys, report schema, scoring behavior, and generated artifacts as part of the upgrade review. Because 0.2.1 is a public beta, avoid treating undocumented behavior as a stable interface.
Keep the policy evaluator versioned separately from the scanner configuration when that makes reviews clearer. This lets you distinguish a change in what DataGuard detects from a change in what your organization is willing to block.
Frequently Asked Questions
Can a GitHub Actions artifact be used as the only audit record?
Artifacts preserve the files from a workflow run, but retention, permissions, and expiration are controlled by GitHub settings. For regulated processes, decide whether the JSON report also needs to be copied to an approved long-term evidence store.
How do I test a quality gate without connecting to a database?
Use checked-in CSV or JSON fixtures that represent both compliant and deliberately invalid cases. Run the scanner and policy evaluator on those fixtures in pull requests, then reserve database-connected jobs for environments where credentials and data access are controlled.
What should happen when a schema change is intentional?
Review the schema snapshot or drift finding as part of the same change that updates the producer and consumer contracts. Update the approved contract and its policy together, rather than weakening the gate globally to make one migration pass.
Does the quality score replace individual rules?
No. A score can summarize overall quality, but it may hide which specific condition caused risk. Keep explicit blocking rules for obligations such as required fields, allowed values, and contract-preserving schema changes.
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