KairoAI Systems
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Data Quality

Unit testing data using AWS services

Build automated data quality suites without leaving AWS.

What this covers

We detail how to deploy repeatable tests, orchestrate them with pipelines, and surface results for engineers and auditors.

Implementation trail

  • Test framework selection
  • Integration with Glue and EMR
  • Continuous validation pipelines
  • Result observability
  • Governance and remediation

Select the right testing toolkit

  • Adopt Great Expectations, Deequ, or AWS Data Quality rulesets depending on schema complexity and scale.
  • Package tests as reusable modules stored in CodeCommit or GitHub with clear ownership metadata.
  • Provide starter templates for common patterns like null checks, referential integrity, and statistical thresholds.

Embed tests into data pipelines

  • Invoke test suites as Glue job steps or Lambda functions triggered by S3 events.
  • Make tests part of pipeline CI/CD so code cannot ship without passing data checks.
  • Quarantine failed datasets automatically and notify owners with actionable context.

Observe and govern quality metrics

  • Publish results to CloudWatch Metrics and EventBridge to power dashboards and alerts.
  • Store historical test runs in DynamoDB for trend analysis and compliance evidence.
  • Document remediation procedures and expected recovery times for each critical dataset.

Raise your data quality bar

We integrate testing frameworks, governance workflows, and alerting into your AWS pipelines so data issues surface before they hit production.

Automate your data tests