This repository demonstrates a structured approach to scaling engineering support for distributed data platforms using controlled failure injection, observability-driven validation, automation, and runbook standardization.
The objective is to model operational maturity practices that reduce support friction, improve incident resolution efficiency, and strengthen platform resilience.
The lab simulates a Kafka-based distributed ingestion system with:
- Synthetic event producers
- Controlled broker failure testing
- Consumer lag impact validation
- Automated evidence collection
- Structured runbook-driven remediation
- Post-incident documentation workflows
- Infrastructure-as-Code extension via Terraform
- Start the platform with Docker Compose
- Generate controlled event load
- Simulate Kafka broker failure
- Capture structured incident evidence
- Quantify consumer lag impact
- Generate automated incident summary
- Document findings via runbook + postmortem templates
data-platform-reliability-lab/
scripts/ → Failure simulation & automation
runbooks/ → Standardized incident response procedures
docs/ → Incident + postmortem templates
terraform/ → Infrastructure-as-Code skeleton
Reliable platforms are not sustained by architecture alone.
They are strengthened through measurable feedback loops, disciplined documentation, and automation-driven support workflows.
Neco Thomas
Cloud & Reliability Engineer
Specializing in distributed data platforms, support automation, and operational maturity engineering.
This project models scalable support patterns for Kafka-based systems, emphasizing observability-driven triage and infrastructure-as-code design.