A CTOs Perspective on How To Scale With Sentry SaaS
Bigeye is the data observability platform that teams at companies like Zoom and Instacart use to keep their data pipelines fresh, high quality, and reliable. Their users depend on them to detect problems in their data pipelines 24/7 and to keep data reliable enough for production use cases of analytics and machine learning.
In this environment, margins for error are razor thin and waiting for a customer to let you know that something isn’t working means it’s already too late.
Read on to learn how Bigeye:
Saves 18-24 engineering hours per month
Preemptively monitors the performance of their applications and,
Leverages Sentry to understand where to focus resources to best serve the business
It’s prohibitively expensive to wait until a customer comes to you and says ‘hey, there’s an error,’ and a lot of this actually comes from not knowing what the context is around errors. Sentry helps us focus on issues that affect multiple customers and narrow down what we pay attention to. Egor Gryaznov, CTO & Co-founder, Bigeye.
Success at scale
When they started looking for an error monitoring tool the Bigeye team was still relatively small, but Egor knew they'd need an extensible SaaS solution that could be customized for unique use cases as the business grew, and one that supports native Java integrations for instrumenting the applications they write.
In terms of efficiency, we didn’t want to manage infrastructure and needed it to be easy to extend into anything else we would want to do as the business grew. We currently have services running across three languages and Sentry has SDKs for them all - this gives us the confidence to say that, were we to build anything new, Sentry would be able to monitor it.
Context is key
Engineering time is better spent solving product problems and building things that make life better for Bigeye’s customers, rather than monitoring deployments or keeping an eye on errors. An example of this is how developers work their way back after identifying an issue using breadcrumbs to retrace their steps, quickly pinpointing where in the timeline things broke and fixing the issue.
We save up to 4 hours per week on average for our on-call engineers and up to 2 additional hours per week just on project-related debugging.
Leveraging Sentry’s Typescript and React support, engineers get more context around errors by creating an introspective wrapper around function calls and log parameters as they go through their stack. Once they receive an error report, the context already includes their custom parameters, significantly speeding up time to resolution.
Working smarter
Metric alerts let the team set thresholds so that only once a certain volume of errors occur, they trigger an alert. They’ve taken this a step further and customized these alerts based on where they would have the biggest impact on their customers, helping them allocate engineering resources more efficiently.
With Sentry, Bigeye has sped up the time it takes to resolve critical issues affecting user experience, saving time that could be better spent building awesome products for their users. On top of that, Egor’s team developed new strategies to better understand where to focus their attention to best serve their customers.
We have, over time, used Sentry to focus more on issues that affect multiple customers, and have really been able to narrow down what we pay attention to in Sentry to focus on the big ticket items rather than trying to knock out random one-off issues.
You’ve seen how Sentry helps Bigeye detect and resolve issues, but how do you know if your data has any issues? Bigeye is a fast and easy way to get visibility into the state of your data stack, check them out!
If you’d like to learn more about how Bigeye gets the most out of Sentry, check out our full conversation with Egor here.
