Best practices, guides, and insights for staying on top of what matters across your company.
Most postmortems identify symptoms, not causes. This post explains why traditional root cause analysis fails in modern systems (especially microservices) and introduces a faster, data-driven approach using dependency mapping and event timelines to find root causes in minutes instead of hours.
Your system doesn’t fail randomly; failures are connected. A deployment triggers an error, which triggers alerts, which escalates into an incident. This guide explains how event correlation works, why most teams don’t implement it properly, and how correlating signals across tools reduces diagnosis time by 70%.
When a major incident hits, someone has to be in charge. Not "in charge" in the sense of knowing the most about the systems - in charge in the sense of coordinating the response, making decisions under pressure, and keeping the team moving toward resolution. That's the incident commander. It's one of the most impactful roles in incident management and one of the least understood by engineers who haven't had to do it.
Two engineers look at the same production alert and disagree on whether it's a SEV1 or SEV2. One wants to wake up the VP of Engineering. The other wants to handle it quietly. Both are wrong - not because of their technical judgment, but because their organization hasn't defined what SEV1 means clearly enough for two people to reach the same answer from the same data.
These two terms get used interchangeably in most engineering conversations - but they describe different things, and conflating them creates real gaps. Incident response is the real-time process of detecting and resolving a production problem. Incident management is the broader discipline that governs how your organization handles incidents before, during, and after they happen. The investments that improve each one are different.
Your status page shows 99.9% uptime. Your customers are still complaining. That's the reliability vs. availability gap - and it trips up a lot of engineering teams. Availability is a number you can put on a status page. Reliability is whether your system actually does what users need it to do, consistently, over time. The two are related but not the same.
Ask five people at your company what an SLA is and you'll get five different answers. Some say it's a customer contract. Some say it's your uptime target. Some use it for internal response time goals. The confusion is common - but getting the distinction right matters for how you set goals, hold teams accountable, and communicate reliability to customers who depend on it.
MTTR dropped from 40 min to 10 min. But that's only 70% of the picture. The real win: engineers sleeping through on-call shifts. Mean time metrics are the most tracked reliability numbers in engineering - and the most misunderstood. This guide covers what each one actually measures, how to calculate them correctly, and how to use them to drive real improvement instead of just better-looking dashboards.
Most systems generate hundreds of metrics. Most of them don't tell you whether users are having a good experience. Google's four golden signals cut through that noise - latency, traffic, errors, and saturation are the four metrics that, together, catch virtually every meaningful failure mode. Here's how to measure and alert on each one correctly.
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