When I first started working with monitoring tools, I thought collecting CPU and memory metrics was enough.
If the dashboards were green, everything was fine.
Then production happened.
Applications slowed down while CPU utilization looked normal. Customers experienced failures before any alert fired. Logs were scattered across multiple servers, making troubleshooting frustrating and time-consuming.
That's when I realized something important:
Monitoring tells you that something is wrong. Observability helps you understand why it's wrong.
Over the past few months, I've been building and improving open-source observability platforms using Grafana, Prometheus, Loki, Docker, Alloy, Kubernetes, and GitOps across both Azure and AWS environments.
A few lessons I've learned:
- Metrics show what is happening.
- Logs explain why it happened.
- Traces reveal where the problem started.
- Good dashboards reduce the time spent investigating incidents.
- Clear alerting prevents unnecessary noise and helps teams focus on what matters.
One of the biggest improvements I made recently was centralizing logs from multiple Docker containers into Grafana using Loki and Alloy. Instead of SSH-ing into different servers to inspect logs, I could search everything from one place, making troubleshooting significantly faster.
This Dev.to blog is where I'll be documenting practical projects, lessons learned, and real-world implementations around:
- DevOps
- Site Reliability Engineering (SRE)
- Kubernetes
- Docker
- Grafana
- Prometheus
- Loki
- AWS
- Azure
- Infrastructure as Code
- Incident response
- Cloud observability
Everything I share will be based on hands-on experience, including what worked, what didn't, and the lessons I learned along the way.
If you're interested in building reliable cloud platforms and improving observability, I'd love to connect.
What's the biggest observability challenge you've faced recently?
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