How Endeavor Streaming Accelerates Observability with

The platform development team at Endeavor Streaming has a critical mission — from balancing operation of the company’s leading digital video platform, at scale, to ensuring everything in their complex cloud environment is performing as expected.

Enabling the company to confidently build on top of its platform and continue to evolve their product delivery is thereby also dependent on maintaining detailed visibility into its supporting cloud applications and infrastructure.

“That delicate balance of operability and development is a big focus for me,” says Claudio Ferrete, Vice President of Platform Development at Endeavor Streaming.

Additionally, the organization dedicates significant resources to ensuring that its platform has what it takes to accommodate all the different use cases they need to support.

“We have customers all over the globe, and different regions have very different types of capabilities when it comes to cloud computing, availability to network connectivity,” Claudio says.

To stay focused on ensuring engineering resources are concentrated on building and operating its own products, versus maintaining tools and solutions to support its goals, Endeavor Streaming uses to aid in numerous observability disciplines, including metrics.

How Meets the Mark for Endeavor Streaming’s Metrics Usage

Claudio notes that Endeavor Streaming’s application and platform are divided into multiple components with well-defined purposes, with functionality designed to ensure the right visibility and insight into each component. That includes maintaining a high-level view of how the video platform is behaving as a whole, and also having the ability to deep dive into each component to dissect and triangulate the data needed to understand any issues that are detected.

“Having the ability to deep dive is crucial for us because due to the complexity of our platform… there’s so many different services, so many different areas and code bases that can generate problems. It’s essential for us to be able to pinpoint the origin of issues as quickly as possible…making sure that everything is performing as we need,” Claudio says.

Beyond log aggregation and management, Endeavor Streaming is expanding its observability practices by increasing its utilization of metrics, and has supported that element of its strategy.

“We’re heavily investing in–and increasing–our level of metrics, and not just in terms of quantity but also in quality,” Claudio says. “We had, from the very beginning, a very high-level, macro view in terms of telemetry. We want the ability to deep dive, go into the detail and telemetry of the different components, including at the code level…We monitor the performance of specific queries, because they’re so fundamental to our performance, that we have to be very aware of how these individual micro level items are performing because they are very significant to the overall platform experience.”

To support its push for increased quality in its metrics, Claudio’s team is attempting to foster far greater insight at the functional level. To that end,’s capabilities have made the process easier for Endeavor Streaming. They’re attempting to shift from a high-level macro view of telemetry to one that goes into deep detail at the function level.’s capabilities have made the process easier for Endeavor Streaming.

“The amount of documentation provided and the standard library components made available for us allows us to very quickly integrate with our code base, which is really helpful,” Claudio says. “This lets us start shipping metrics very easily and very quickly. It allows us to move on from an experimentation stage where we’re understanding how the product works to very quickly being in a position where we’re actually shipping production metrics and we’re actually using the platform in production.”

Endeavor Streaming is also relying heavily on’s pre-built dashboards for metrics as a starting point, and quickly adapting them to meet their unique requirements. The expert said the ability to avoid starting from scratch and deploy the platform in its production environment faster thanks to the pre-built dashboards was another significant benefit. 

With logging and metrics work underway, Claudio says Endeavor Streaming would also like to migrate their current “home-brewed” solution for distributed tracing into use of’s Distributed Tracing solution.

To learn more about how Endeavor Streaming is utilizing to push its observability practices forward—including in log aggregation, platform engineering and Kubernetes monitoring, download and read the full case study.

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