Using Spark, Airflow, or Beam to power your data infrastructure? Let us show you how Sentry helps data engineers and data scientists identify and fix errors quickly within data pipelines.
Application monitoring and observability 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.
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.
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.
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.
Just look at all the high-quality security
features all accounts get, regardless of plan.