October 10, 2023
Datadog, the observability platform used by thousands of companies, runs on dozens of self-managed Kubernetes clusters in a multi-cloud environment, adding up to tens of thousands of nodes, or hundreds of thousands of pods. This infrastructure is used by a wide variety of engineering teams at Datadog, with different feature and capacity needs.
How do we make sure that tens of thousands of nodes, with very different specifications and on different clouds are healthy, updated with the latest security patches, and running an updated version of the kubelet and container runtime, without breaking applications or interrupting more than a thousand engineers that rely on this infrastructure for their daily job?
In this session, Ara Pulido, Staff Developer Advocate, will chat with Adrien Trouillaud, Engineering Manager and David Benque, Staff Software Engineer, both part of the Compute team, about their strategies, lessons learned, and practical tips on how to successfully manage a huge fleet of Kubernetes nodes.
By the end of the session you will have a set of tips on how to prepare when scaling your Kubernetes clusters to hundreds or even tens of thousands of nodes.
Datadog on Data Science
Datadog on Kubernetes Autoscaling
Datadog On Maintaining eBPF at Scale
Datadog on Caching
Datadog on Data Engineering Pipelines: Apache Spark at Scale
Datadog on Site Reliability Engineering
Datadog on Building an Event Storage System
Datadog on gRPC
Datadog on Rust
Datadog on Profiling in Production
Datadog on Gamedays
Datadog on Chaos Engineering
Datadog on Agent Integration Development
Datadog on eBPF
Datadog on Serverless
Datadog on Kubernetes Monitoring
Datadog on Software Delivery
Datadog on Incident Management
Datadog on Kubernetes