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Operational case files

Real production work, anonymized for public review.

A selection of production case studies — what was happening, what I did, and what it demonstrates. Public-safe wording, no internal identifiers.

All production details are anonymized. The technical shape, layers and operational decisions are preserved.

Production details are anonymized to protect internal systems and customer environments. Public-safe wording, no internal identifiers.

P2 case_file/001
Open →

Deployments unblocked

Production CI/CD Failure in a Reusable GitHub Actions Workflow

A production deployment pipeline used a reusable GitHub Actions workflow to standardise releases. Releases had been working, then began failing before any job ran.

impact

Deployment workflow restored. Failure mode documented so the next contract drift is diagnosed in minutes instead of trial-and-error against the pipeline.

CI/CD Production
P2 case_file/002
Open →

Production reliability improved

Elasticsearch Capacity Planning for High-Traffic Event Readiness

Three production Elasticsearch nodes in Azure needed to be sized for an upcoming high-traffic event. The cluster fed search and analytics paths the event would amplify.

impact

Cluster prepared and validated for the event window with a documented rollback path. Sizing decisions captured for future event-readiness work.

Cloud Database Observability Production
P3 case_file/003
Open →

Incident stabilized

Proactive Log Management Across Production Services

Multiple production services — Elasticsearch, RabbitMQ, Nginx — were emitting logs at a rate that would, without intervention, eventually pressure disk capacity and trigger reactive incidents.

impact

Disk-usage risk reduced across the affected fleet. The initiative removed a class of avoidable late-night incidents and made log volume a planned cost instead of a surprise.

Linux Observability Production Storage
P1 case_file/004
Open →

Incident stabilized

Kubernetes CrashLoopBackOff Production Recovery

A production Kubernetes cluster running in Azure (AKS). A Zabbix alert reported pods of an application component down. CrashLoopBackOff was active.

impact

Component restored to a healthy ready state. Diagnostic path documented for future incidents of the same shape.

Kubernetes Cloud Observability Production
P2 case_file/005
Open →

Production reliability improved

Monitoring Agent Migration at Scale

A fleet of production Linux servers was running a monitoring agent version approaching end of support. The migration covered messaging, DRM, licensing and backend services.

impact

Approximately 50 hosts were migrated by Lautaro. No rollback was required, and no host lost monitoring visibility for more than approximately two minutes.

Linux Observability Automation Change Management
P2 case_file/006
Open →

Production reliability improved

De-risking a Legacy CI/CD Deployment Path

Several production deployment jobs used a legacy SSH jump host as an intermediate step before reaching their runner or target environment.

impact

Approximately 15–20 jobs have been migrated so far. The work remains in progress, so the final retirement of the host is intentionally not claimed.

CI/CD SSH Reliability Modernization
P2 case_file/007
Open →

Operational cost improved

Temporary Capacity Doubling for a High-Traffic Load Test

A UAT environment needed to run a representative stress test before a high-traffic event, but its standard capacity was not enough to model the expected load.

impact

The stress test passed without issues. The temporary capacity increase provided a representative test profile without leaving permanent overprovisioning afterward.

Capacity Planning Performance Cloud Change Management
P1 case_file/008
Open →

Incident stabilized

RabbitMQ Backlog and Controlled Message Flush

Consumers connected to a production RabbitMQ cluster were not processing messages fast enough. The backlog grew and the affected node became stuck under resource pressure.

impact

The manual flush discarded queued messages but prevented the stuck node from continuing toward a more severe resource-exhaustion condition. The exact recovery time is not documented.

RabbitMQ Linux Incident Response Observability