I wrote a practical guide to observability — would love feedback

Hey folks,

I’ve been working on backend infrastructure and real-time data pipelines (Flink, Kafka, Spark, AWS) at my org for the past few years. A big part of my work involves improving observability, not just collecting logs and metrics, but actually making systems debuggable and reliable at scale.

So I decided to write a hands-on guide to observability. It’s aimed at engineers who want to learn more, people who actually want to reason about what to observe, why P95 not P99, how to balance logs vs traces, and what “good observability” means in practice.

Here’s Part 1: 👉 https://medium.com/@lakhassane/understanding-observability-key-components-and-benefits-ddf5a836ef49

Would love feedback or critiques, especially from those who’ve had to do similar things or are just interested. I plan to write follow-ups on metrics, traces, and common failure patterns.

Thanks