Observability Closing the Loop: The Graduated Case for Self-Healing Systems Kubernetes self-heals at the infrastructure layer. AI-driven remediation is at the frontier. Between them is a trust ladder most teams skip. The step they skip most consistently is Learn — and that's why the same incidents keep coming back.
Observability Golden Paths to Observability: What Platform Engineering Actually Delivers (and What It Doesn't) DORA's 2024 data shows internal developer platforms reduce change throughput by 8% and stability by 14%. Build the platform anyway — but for governance and consistency, not for productivity. Here's why the distinction matters.
Observability Wide Events, eBPF, and Why the Three Pillars Are a Tax You're Still Paying Storing the same request as a metric, a log, and a trace costs you three times. Observability 2.0 and eBPF attack the same problem from different angles — here's when each makes sense and what the cost model actually looks like.
Observability Datadog, Dynatrace, and the Bill That Ate Your Engineering Budget Standardized telemetry made it easy to collect everything. The APM vendor boom made it expensive to keep it. The observability tax is now an architectural decision — here's how to model it before you get the bill.
Observability Metrics, Logs, Traces — and the War That Standardization Won The three-pillars model organized the early observability market — and provoked the standardization that ended vendor lock-in. OpenTelemetry didn't just solve fragmentation. It changed the economics of the field.
Observability The 2019 Baseline: What the Books Said vs. What Your Team Actually Ran In 2019, a wide and well-documented gap separated what DORA and Google SRE recommended from what most engineering teams actually ran. Here's what the baseline looked like — and why it matters for benchmarking where you are today.