Distributed tracing is quickly becoming an invaluable part of a modern observability solution by providing a way for developers to understand how requests move through their systems. This ability is increasingly critical in a world where a single request or transaction may touch tens of microservices spread across different containers, environments, and cloud providers.

Grafana Lab’s open source tracing database, Tempo, is a scalable, cost-efficient solution for storing and querying distributed traces generated by any of the open source tracing protocols, including Jaeger, Zipkin, and OpenTelemetry.

But enterprises often need additional features — including access controls, indemnification, and support guarantees — in order to push broader adoption. Many companies can’t use a SaaS solution and need something they can host themselves.

And that’s where Grafana Enterprise Traces comes in.

Grafana Enterprise Traces (GET) is built on a unique approach to trace indexing, storage, and administration control that allows companies to run it securely at scale. Everyone in an organization can access all of their relevant trace data, and companies that have specific security policies or are in regulated industries can leverage the built-in Grafana interface to easily manage permissions and settings and grant individuals access to the resources they need without compromising cost.

During this webinar, you’ll learn about:

  • The challenges when it comes to securing and scaling distributed tracing tools for large enterprise companies
  • How Grafana Labs can help increase internal adoption around distributed tracing with features like access controls, indemnification, and more 
  • What new features are included in GET through a live product demo
  • How to get access and try GET for yourself

Aengus Rooney

Senior Solutions Engineer, Grafana Labs

Aengus has been involved in all aspects of data systems, enterprise data, big data, and cloud data platforms, for over two decades. Originally programming large scale transactional databases, his career moved into parallel & distributed computing, advanced analytical systems, and observability. Aengus has been responsible for the technical strategy, architecture, design, and engineering for multiple enterprise scale data systems, using proprietary and open source technologies.