Operations | Monitoring | ITSM | DevOps | Cloud

It’s gotten to the point where Black Friday reliability prep has to start on…well Black Friday. This year, 32% of consumers in the US claimed that they were going to start their holiday shopping in July-October. Plus, Black Friday isn’t the only day eCommerce businesses have to worry about, now we have Cyber Monday, Travel Tuesday, and the thousands of Prime Days from Amazon.

Building an AI proxy for our AI features was one of the best decisions we made a year ago. In this article, we will share why and what challenges we faced.

Advances in artificial intelligence are rapidly transforming the IT operations landscape. According to Enterprise Strategy Group, 85% of organizations use or plan to deploy AI across many functional areas, including IT operations. Among its many benefits, AI can help ITOps teams: AI has immense potential to transform how IT operations, service management, and infrastructure teams function. Adoption is the first step toward creating organizational change.

The CrowdStrike outage was a reminder of how quickly the dominoes can fall—especially when the foundation is shaky. Delta Airlines was hit harder than its competitors. While United and American Airlines were able to recover within days, Delta faced ongoing struggles, leading to the cancellation of 7,000 flights over five days.

About a year ago, Honeycomb kicked off an internal experiment to structure how we do incident response. We looked at the usual severity-based approach (usually using a SEV scale), but decided to adopt an approach based on types, aiming to better play the role of quick definitions for multiple departments put together. This post is a short report on our experience doing it.

With every job I have, I come across a new observability tool that I can’t live without. It’s also something that’s a superpower for us at incident.io: we often detect bugs faster than our customers can report them to us. A couple of jobs ago, that was Prometheus. In my previous job, it was the fact that we retained all of our logs for 30 days, and had them available to search using the Elastic stack (back then, the ELK stack: Elasticsearch, Logstash, and Kibana).