Casey Lab
Data Engineering Lab
Applied ETL, automation, and platform reliability patterns.
This sandbox is for practical delivery patterns across SQL, SSIS, Azure Data Factory, Databricks, and .NET tooling. Every entry focuses on repeatability, production safety, and faster troubleshooting.
Production-grade thinking in a safe test space.
Playbooks
Reusable runbooks and delivery patterns.
Short guides that can be reused for enterprise data operations.
ETL release checklist
Pre-prod validation, deployment gates, rollback criteria, and post-release checks.
Incident triage flow
Fast root-cause routing for failed jobs, missing files, schema drift, and SLA risk.
Change governance
Ticketing, peer review, and release notes that keep production stable.
Sandbox
Current experiments and reference builds.
Data quality checks
Patterns for threshold checks, schema validation, and exception tracking.
Pipeline resiliency
Retry strategies, idempotency, dependency sequencing, and safe restart points.
Internal utility apps
Small tools for config validation, transfer diagnostics, and operator visibility.
Roadmap
What is next in the lab.
Weekly pattern notes
Architecture notes with practical implementation snippets.
Starter template packs
Downloadable SQL, ADF, and troubleshooting templates.
Observability examples
Monitoring dashboards with failure categories and SLA trends.