Data Science for Infrastructure: Observe, Understand, Automate

Pixie, an open-source observability platform for Kubernetes, can be used to turn low-level telemetry data into high-level signals about system health and automate workflows.

• Observability in cloud-native applications is a data analytics problem
• Pixie uses automated telemetry using eBPF to capture application, network, and infrastructure data with low overhead
• Pixie is 100% scriptable and API-driven, allowing for infrastructure as code and easy integration with downstream tools
• Pixie can be used to generate high-level signals about system health and automate workflows, such as detecting SQL injections and K8s deployment autoscaling

Pixie was built to solve the problem of understanding and automating workflows in complex cloud-native applications. By utilizing automated telemetry and a scriptable, API-driven system, Pixie can generate high-level signals about system health and automate workflows, such as detecting SQL injections and K8s deployment autoscaling.

Cloud-native applications today are increasingly complex and therefore increasingly hard to understand. It’s critical to connect decisions around resource allocation and architecture to business metrics such as end-user latency, but very difficult to do in practice. Ultimately, understanding how your systems behave and why is a data analytics problem. Like most data analytics problems, the trick is in collecting and wrangling the right data sources.In this talk, you will learn how Pixie, an open-source observability platform for Kubernetes, can be used to painlessly turn low-level telemetry data into high-level signals about system health. The talk will also show these high-level signals can be used as input to a variety of use cases, such as detecting SQL injections and K8s deployment autoscaling.