Observability — something data engineers enjoy

Application monitoring and observability typically focus on full-stack applications, like user interfaces within browser and mobile applications, server-side applications, and microservices.

We've found that many of the same principles are equally important for engineers building data platforms — yet the unique needs of these teams are overlooked.


Aggregate processing errors

Bad data happens to everyone. Even us. And, also you. In the midst of inevitability, does your data platform alert you to issues?

Sentry identifies errors, providing necessary context to resolve unexpected data within a data pipeline. Better yet, we group common errors, reducing the noise from ten thousand alert emails to one.

A screenshot of the a Sentry breadcrumb list.

Batteries included

With native integrations for common data platforms, such as Apache Airflow and Apache Beam, getting started with Sentry takes less than five minutes.

Deploy ETL jobs and data pipelines with the confidence that exceptions won't get buried in log files.

A screenshot of the Sentry interface showing a Beam error.

Deep Context

Do your log lines have all the information you need to fix data quality issues?

Sentry equips data engineers and data scientists with context needed to identify issue resolutions and ship a fix quickly.

A screenshot of the Sentry interface showing issue tags.
1,000,000 developers at over 30,000 companies already use Sentry. Why don’t you?