CI/CD Integration & Change-Impact Analysis
BI Regression Guardrails
Built CI/CD-integrated regression guardrails that ingest BI metadata and data pipeline code to build dependency graphs. Implemented automated regression tests for key metrics and dashboards, with change-impact analysis that flags risky PRs before merge.
Challenge
Organizations ship dbt/SQL/ETL changes that silently break executive dashboards or KPIs. Traditional data observability tools monitor tables but rarely test business logic and dashboards end-to-end, leaving analytics teams exposed to regressions that reach production.
Solution
Built CI/CD-integrated regression guardrails that ingest BI metadata and data pipeline code to build dependency graphs. Implemented automated regression tests for key metrics and dashboards, with change-impact analysis that flags risky PRs before merge.
Impact Metrics
Results
- • 45+ Regressions Prevented before reaching production
- • 200+ Dashboards Protected with automated regression tests
- • 100% of Risky PRs Caught before merge with automated blocking
- • <5min Change-Impact Analysis time for any code change
- • LLM-Generated Summaries for non-technical stakeholders
Business Impact
Eliminated production dashboard breakage, reduced time to assess change impact from hours to minutes, and built trust with executives by catching regressions before they go live. Enabled faster, safer data pipeline deployments.
Regression Guardrails System
A proven approach combining statistical rigor, automation, and AWS best practices.
Dependency Graph Building
Ingest BI metadata (QuickSight/Tableau) and data pipeline code (dbt/SQL) to build comprehensive dependency graph from PR → model → table → metric → dashboard tile.
Impact Analysis
On each PR, automatically identify which dashboards and metrics are affected by upstream changes. Generate risk scores based on dashboard importance and change magnitude.
Regression Testing
Run numerical diff tests for key metrics (pre/post change), structural checks for dashboards (tile presence, filters, visuals), and data quality validations.
CI/CD Integration
Integrate with GitHub Actions, GitLab CI, or AWS CodeBuild to automatically run tests on PRs. Fail PRs or flag with risk scores when thresholds are exceeded.
LLM Summaries
Use Amazon Bedrock to generate human-readable change-impact summaries for non-technical stakeholders, explaining what changed and why it matters.
Technology Stack
- GitHub Actions / GitLab CI / AWS CodeBuild
- AWS Step Functions for test orchestration
- Python for test execution and analysis
- Amazon Bedrock for LLM summaries
- Aurora PostgreSQL for metadata storage
- QuickSight/Tableau APIs for BI metadata
- dbt/SQL parsing for pipeline analysis
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