At incident.io, we run on a monolith. This brings a whole load of benefits that we don’t want to give up any time soon. We don’t have to worry about the speed of internal network requests, complex deployments, or optimizing work that touches multiple services. This blog post isn’t about the relative benefits of monoliths though (but we’ve written more about that here if you are interested)! Ownership in monoliths is tricky.

As a provider of incident management software, we at incident.io manage sensitive data regarding our customers. This includes Personally Identifiable Information (PII) about their employees, such as emails, first names, and last names, as well as confidential details regarding customer incidents, such as names and summaries. Consequently, we approach the management of this data with a great deal of care.

We've written several times about our data stack here incident, but never about our underlying data warehouse and the design principles behind it. This blog post will run through the high-level structure of our data warehouse and then will go in-depth into the underlying layers.

Writing a meaningful update for customers every week has been held sacred at incident.io since we started the company. We've written over 200 of them in the past 4 years, and we recently celebrated going 2 years straight without missing a single a single week The numbers themselves are not the goal, but the consistency of this habit and what it represents for our customers and our team is very real, and special to me.

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).