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DataGuard AI can run repeatable data-quality checks in GitHub Actions, produce JSON and HTML reports, and expose problems while a pipeline is being developed. However, a successful scan is not automatically a failed build when the data violates a rule. The scan step proves that execution and report generation completed; a separate policy step must read the JSON report, apply thresholds your team has chosen, and decide whether the workflow should block a pull request or deployment.
That distinction shapes a safe implementation. Install the pinned Python package in the runner, scan a supported file or database result, upload the reports as artifacts, then add explicit policy evaluation. DataGuard AI is listed on PyPI as public-beta version 0.2.1, released October 3, 2026, so validate its output against your own contracts before treating it as a production control.
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What is DataGuard AI, and why run it in GitHub Actions?
DataGuard AI is an open-source Python toolkit for data quality and governance. Its documented workflow combines deterministic detection with optional AI-generated explanations and remediation suggestions. The project describes the philosophy as: "AI assists; deterministic and statistical checks detect and verify." Core validation therefore does not require an external language model.
Putting a scan in GitHub Actions gives a repository a repeatable early-warning loop. A pull request that changes an extraction script, fixture, transformation, or data contract can generate the same report every time the workflow runs. Reviewers can inspect machine-readable findings in automation and an HTML rendering for people.
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The useful scope is development and delivery feedback, not a claim that the package replaces operational monitoring. It fits repositories that keep sample or test datasets, teams experimenting with contracts, ETL development, governance checks during development, and demonstrations of CI/CD data controls. Production observability still needs its own monitoring, alerting, and incident process.
Version 0.2.1 is identified as a public beta. Its API, configuration schema, scoring model, and command behavior may change before a stable 1.0 release. Pin the version you have evaluated and review release notes before upgrading.
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Use four separate stages: install, scan, publish reports, and enforce policy. Keeping these stages distinct prevents a report-generation success from being mistaken for a data-quality pass.
- Install a pinned package. In the runner environment, install the DataGuard AI version approved by your project. Pinning makes a pull request reproducible and avoids silently changing detection behavior when a new beta build is published.
- Run the scan. Point the documented scanner at a supported dataset or configured source and request a JSON report. Generate HTML as well when reviewers need a browsable summary. The exact invocation and configuration keys should come from the version you pinned; do not copy flags from an unrelated release.
- Upload both reports. Configure the workflow to retain the JSON and HTML files as workflow artifacts. JSON is for later automation; HTML is for a developer who wants to inspect individual findings after the job finishes.
- Evaluate policy separately. Add a later step that reads the JSON report, compares findings with thresholds agreed by the team, and exits with the status your branch or deployment rules expect. A scan can be green while this policy step is red.
The author tutorial states the distinction plainly: "The scan step succeeds if execution and report generation succeed. Enforcing a quality gate requires an additional policy step that evaluates the JSON report against agreed thresholds." Treat that as an implementation rule, not as an implicit behavior of the scanner.
Which files and databases can DataGuard AI scan?
The package description lists CSV and JSON support, optional Parquet support, and scans of selected DuckDB and PostgreSQL tables or query results. Database selections are loaded into runner memory, which is important when deciding whether a CI job can handle a particular dataset.
| Source | Documented position | What to decide in CI |
|---|---|---|
| CSV | Supported | Keep representative fixtures small enough for repeatable runner execution. |
| JSON | Supported | Define whether nested structures are flattened or checked according to your chosen configuration. |
| Parquet | Optional support is described | Confirm the pinned beta version and its dependencies before making it a required gate. |
| DuckDB | Selected tables or results can be scanned | Limit the selection to data the runner can safely load into memory. |
| PostgreSQL | Selected tables or results can be scanned | Use a controlled CI connection and evaluate sensitivity, cost, and result size first. |
Warehouse-scale pushdown profiling is described as a roadmap item rather than an available capability. Do not assume that pointing a workflow at a production-sized table will move computation into the database.
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What checks should I configure?
Built-in detection covers common structural, statistical, freshness, and governance risks. Reusable YAML rules provide a way to express the contract that matters for a particular dataset.
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| Check or rule | Use it for | Important qualification |
|---|---|---|
Null detection and not_null |
Required fields such as event time or customer key | Set the rule only when the domain contract truly requires a value. |
| Duplicate rows | Unexpected repeated records | Decide whether duplicates are globally invalid or expected in a staging table. |
Uniqueness and unique |
Keys that must identify one record | Do not assume every column ending in _id is unique; foreign keys can repeat. |
accepted_values |
Enumerated statuses, regions, or event types | Document how new legitimate values are introduced. |
between and range checks |
Numeric or date bounds | Include units, timezone assumptions, and inclusive or exclusive boundary semantics in the contract. |
regex |
Format validation for identifiers or strings | Keep patterns specific enough to catch errors without rejecting valid variation. |
max_null_pct |
Allowed missingness for a column | Choose a percentage based on business tolerance, not a universal default. |
row_count_between |
Expected volume bands | Account for weekends, holidays, backfills, and intentionally small test fixtures. |
| Freshness risks | Late or stale data | Define the reference timestamp and acceptable lag for each source. |
| Likely PII detection | Potentially sensitive fields | Treat detections as review signals; confirm classification with your privacy owner. |
The toolkit also describes quality and governance scoring, schema snapshots and drift detection, and generation of starter dbt tests, YAML contracts, and Great Expectations configuration. Generated artifacts are starting points. Review names, keys, null semantics, allowed values, and ownership before committing them as policy.
Should I publish JSON, HTML, or both?
Publish both when the workflow is used by engineers and automated policy code. They answer different questions.
- JSON: stable input for a policy evaluator, dashboards, or a later workflow step. Your evaluator should read the report schema produced by the pinned package rather than assuming fields from a different beta release.
- HTML: a human review surface. It is useful when a pull-request author needs context about which column, rule, or sample triggered a finding.
- Workflow artifacts: the hand-off between the run and its reviewers. Name artifacts by workflow and commit so a report cannot be confused with another branch’s output.
Keep report generation independent from the decision to block. If HTML rendering fails, that is an execution problem to fix; if a rule exceeds an agreed threshold, that is a policy result to communicate distinctly.
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How do I turn the JSON report into a quality gate?
First define the policy in words, then implement it against the report. DataGuard AI does not prescribe universal thresholds, and a score alone cannot express every business rule.
1. Define severity and ownership
Agree which findings are blocking, warning-only, or informational. For example, a missing primary key might block a release, while a small increase in nullable marketing metadata might create a warning. Assign an owner for every blocking rule so a red check has a recovery path.
2. Define numerical thresholds
Write down permitted null percentages, row-count bands, freshness lag, accepted values, and range boundaries for each dataset. Include exceptions for planned backfills or intentionally small fixtures. The threshold belongs to the data contract, not to an arbitrary property of the CI runner.
3. Make the evaluator conservative about unknowns
If a report field is missing, a rule is not recognized, or the beta schema changes, fail closed for critical datasets or route the result to an explicit review state. Do not silently treat an unreadable finding as a pass.
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4. Return a useful status
The policy step should distinguish scanner execution errors from data violations. In a pull request, print the violated rule, observed value, allowed value, and report location so the author can fix the data or contract without opening several artifacts.
5. Test the gate itself
Use fixtures that deliberately contain a null, duplicate, invalid enum, out-of-range value, stale timestamp, and unexpected row count. Verify that each fixture produces the expected policy result, then test a clean fixture to ensure the evaluator does not block valid data.
A report can contain a quality score while your gate still needs rule-level decisions. Scores are useful for trend or triage; explicit thresholds explain why a particular change is blocked.
Are deterministic checks and AI explanations separate?
Yes. The documented detection path is deterministic and independent of an LLM. Optional AI assistance can explain structured findings or suggest remediation, but it is not required to detect nulls, duplicates, ranges, drift, or other configured violations.
The project says the external provider receives structured findings rather than raw dataset rows. That reduces what is sent compared with transmitting the complete dataset, but it does not eliminate privacy review. Before enabling an external provider, identify whether column names, values summarized in findings, or inferred PII labels are acceptable to send under your organization’s policies.
For a strict gate, keep the pass/fail decision on deterministic report data. Treat generated explanations as review help, and make sure a provider outage cannot convert a valid deterministic result into an unexamined pass.
Can I use this with production data?
Only after a deliberate assessment of scale, sensitivity, and cost. Selected database tables or results are loaded into memory, and warehouse-scale pushdown profiling is not described as available. A production query that is safe in a warehouse may be too large or expensive for a hosted runner.
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- Scale: estimate the result size before the workflow runs and prefer bounded, representative slices for pull-request checks.
- Sensitivity: remove or mask unnecessary personal data; review whether structured findings could still reveal sensitive information.
- Cost: avoid running expensive database scans on every commit when a scheduled or deployment-stage check is sufficient.
- Access: use narrowly scoped credentials and a read-only account for CI.
- Operational coverage: retain production monitoring for freshness, volume, incidents, and downstream service health.
The author tutorial recommends evaluating scanning cost, data sensitivity, and performance before applying the same pattern to large production datasets. That is a decision checklist, not a published benchmark.
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Start with visibility, then graduate to enforcement after the report and contract are trusted.
- Baseline on fixtures. Scan representative clean and intentionally broken datasets. Record which findings are useful and which require configuration changes.
- Pin and review. Lock the tested package version, review its dependency changes, and rerun the fixture suite whenever you upgrade.
- Run non-blocking. Upload JSON and HTML reports while developers learn the failure vocabulary. This stage exposes noisy rules without interrupting delivery.
- Gate one contract at a time. Promote only rules with a named owner, a documented threshold, and a tested remediation path.
- Review generated artifacts. Treat starter dbt tests, YAML contracts, and Great Expectations configuration as drafts that need domain-specific edits.
- Reassess before production. Repeat the memory, sensitivity, credential, and cost review for every new source.
This progression preserves the value of early feedback without pretending that a beta tool has been independently benchmarked or production-tested.
What commonly goes wrong in a GitHub Actions implementation?
The workflow is green even though the report contains violations
That is expected when only the scan and report steps exist. Add a policy evaluator that reads JSON and returns the blocking status your branch or deployment rules use.
The gate blocks every run on a fixture
Check whether production-oriented thresholds were applied to a small test dataset. Row-count bands, freshness windows, and allowed values often need a fixture-specific contract.
An identifier is reported as non-unique
Confirm whether the column is a primary key or a foreign key. A suffix such as _id is not evidence of uniqueness; declare uniqueness only where the dataset contract requires it.
The database job runs out of memory
Reduce the selected result, scan a bounded fixture, or move the check to an environment designed for larger profiling. Do not assume the current in-memory behavior provides warehouse pushdown.
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AI explanations create a governance concern
Keep deterministic checks enabled and disable optional explanations until your privacy and security review approves the provider, the structured findings sent, and the retention path.
A beta upgrade changes the report
Compare the new report schema and scoring behavior with pinned fixtures before merging the upgrade. Update the evaluator only after you understand any changed field or severity.
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Use DataGuard AI in GitHub Actions when you need repeatable checks over development datasets and want reports attached to code changes. Build the workflow as scan, artifact publication, and policy evaluation rather than treating report creation as enforcement. Keep deterministic validation at the center, use AI explanations only when their data handling is acceptable, and move toward production use only after scale, sensitivity, and operational coverage have been assessed.
Frequently Asked Questions
Does DataGuard AI require an LLM to detect a bad column?
No. The documented detection path is deterministic, so checks can run without an external model. AI assistance is an optional layer for explaining structured findings or suggesting remediation.
What happens to a database table during a scan?
The documented limitation is that selected table or query results are loaded into the runner’s memory. That behavior makes result-size planning important and is different from warehouse-side pushdown profiling.
Can generated dbt tests or contracts be committed unchanged?
They should be reviewed first. Generated files are starter artifacts, so confirm keys, nullability, accepted values, ownership, and exception handling against the domain contract before making them authoritative.
Is a quality score enough to approve a deployment?
Not by itself. A score can help with trend analysis, but deployment approval should map explicit blocking rules and thresholds to the JSON findings your team has agreed to enforce.
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