Operations | Monitoring | ITSM | DevOps | Cloud

A few weeks ago we had a major incident. We were releasing our Practical Guide to Incident Management, and after posting about it online an incident.io employee noticed that the page wasn’t loading. Just to set the scene, I’ve been at incident.io for 3 months and don’t have any experience of incidents in my previous role. When the team got paged I expected this to be one of those “follow along and learn how the wizards work their magic” exercises.

IT experts and techies are constantly devising new ways to do more with less in our rapidly evolving world. Traditional platforms monitoring and modern technological maintenance take a large portion of a conventional organization’s IT budget. This leaves limited resources to develop new standards-based and adaptive applications that fulfill core business demands.

To err is human. The process of software development can’t be error-free; fixing errors is part and parcel of building software applications. And, no matter how much you dislike those harsh error messages when your code fails and exits, you have to admit that they save you from a lot worse.

Digital-first businesses are striving for service assurance, which has become the lifeblood for their businesses processes. But they are increasingly getting complex across legacy and cloud-native applications, multi-cloud distributed services, with the rise of edge and when leveraged with Kubernetes and microservices architectures. Service assurance needs full-stack observability; however, customers need an approach to tame the data deluge while enabling actionable insights.

We have a term we like to use when we meet CFOs who have just gotten their biggest AWS bill ever: bill shock. Bill shock is when finance suddenly rings the alarm that the bill is “too high” and gets everyone scrambling to explain what they’re spending money on. It often happens when the bill reaches a new milestone (the first million, ten million, or hundred million) or growth trajectory (it doubled in a quarter!?). The problem with bill shock is that it can be highly disruptive.

This post is the third in a series of deeper dive articles discussing DORA metrics. In previous articles, we looked at: The third metric we’ll examine, Change Failure Rate, is a lagging indicator that helps teams and organizations understand the quality of software that has been shipped, providing guidance on what the team can do to improve in the future.

The MQTT Consumer Plugin is one of our most widely used input plugins for Telegraf. If you need a little bit of background, then I highly recommend checking out the following: I plan to release an MQTT best practices blog soon, but we thought this plugin partnership was too good not to talk about now.

A large U.S.-based insurance provider was experiencing rising database software licensing costs. In order to reduce the software licensing costs, the organization needed to complete a comprehensive infrastructure analysis of over 200 physical servers. 75 percent of these physical servers supported one software application, their database solution. Additionally, the software routinely only utilized between two and four cores, despite having 24 cores on each server.

PostgreSQL is an open-source relational database management system that’s been utilized in continuous development and production for 30 years now. Nearly all the big tech companies use PostgreSQL, as it is one of the most reliable, battle-tested relational database systems today. PostgreSQL is a critical point in your infrastructure, as it stores all of your data. This makes visibility mandatory, which in turn means you have to understand how logging works in PostgreSQL.