Casey Lab
Data Engineering Labs
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
Upcoming mock template packs and lab content.
All items below are sample-only patterns you can reuse and customize.
Python templates
Python validation flow with quality checks and quarantine routing
Playwright templates
Playwright sample: API health endpoint validation.
Release ADF Orchestration Blueprint
Mock pipeline JSON with parameterized source/sink, alerting hooks, and environment promotion notes.
Databricks Notebook Pack and Runbook
Sample notebooks for validation, quarantine routing, and quality-score summaries with troubleshooting steps.
Cross-platform ETL Troubleshooting Kit
Unified checklist covering SQL, ADF, and Databricks failure modes with escalation paths.
Python Data Quality Starter Pack
Starter Python samples for file validation, job health checks, and operational alerting.