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Latest Videos

Building a flexible, realtime data warehouse at Sentry with Beam + Dataflow (Syd Ryan)

Syd Ryan describes two hard problems they've solved at Sentry with streaming Beam pipelines. The first solution combines Postgres change data capture and SQL views to produce a table that appears to be updating in real time within BigQuery. The second solution is aggregating 1000s of events per second and backfilling historical data effectively with Beam's unified batch/streaming interfaces.

Beam in Production: Lessons learned and best practices (Mike Clarke)

Mike describes gotchas and early struggles Sentry hit moving streaming data pipelines off our laptops and into production. He covers some unexpected Beam defaults, detecting schema errors, compare performance between the python & java SDK, and proactively identifying when production pipelines break due to unexpected data.

Sentry for Native Crashes: Gaming, IoT, and Beyond

Application crashes have a significant impact on customer experience, which can adversely affect a company’s reputation and revenue. Error and crash reporting is a unique feedback mechanism that provides true data about the quality of their product. Developer teams that create games, mobile applications, IoT, and other high-performance applications need rich insights into application health to quickly and continuously fix software errors with minimal impact.

Distributed Tracing with Sentry: How to Find the Root Cause of Errors Across Applications

Implementing Sentry on all your services allows you to use distributed tracing to find the root cause of errors. Just because an error happens in a browser or mobile app, doesn’t mean the issue is with the frontend or mobile code. The issue could stem from an error with code in a different project that they interact with in some way. Distributed tracing empowers developers to find the actual cause of hard to fix issues.