Welcome to our series of blog posts about things Sentry does that perhaps we shouldn’t do. Don’t get us wrong — we don’t regret our decisions. We’re sharing our notes in case you also choose the path less traveled. In this post, we’re giving context to everything that needed to happen before we introduced Snuba, our new storage and query service for event data.

Welcome to our series of blog posts about all the nitty-gritty details that go into building a great debug experience at scale. Today, we’re looking at the shortcomings of source maps. Other than Python, JavaScript is the oldest platform that Sentry properly supports, which makes sense considering many Python services (including Sentry itself) have a JavaScript front-end. As the popularity of transpiling grew, the need for tools to debug transpiled code in production became obvious.

In our Sentry for Data series, we explain precisely why Sentry is the perfect tool for your data team. The present post focuses on how we optimized Airflow for deeper insights into what goes wrong when our data pipelines break. Data enables Sentry’s go-to-market teams by generating high-quality leads and tailored marketing campaigns. Of course, data is also used to steer the business by influencing how we think about Sentry pricing, future opportunities, and feature roadmap.

Errors suck. And you don’t want to spend too much of your time fixing them, dealing with them, investigating them, etc. In our Workflow blog post series, we look at how to optimize your, well, workflow, from crash to resolution. At this point in our workflow (check out the first and second posts in this series), we’ve minimized the impact of errors on the development process by creating infrastructure and culture equipped to handle unexpected issues.