Summary
Capital One successfully leveraged OpenTelemetry to address the complex challenge of managing vast amounts of telemetry data, specifically tracing data, leading to a remarkable 70% reduction in data volumes. Faced with an unmanageable and unaffordable situation of collecting petabytes of data daily, their engineers, Joseph Knight and Sateesh Mamidala, implemented a dedicated infrastructure for tail-based sampling. This strategic shift from less effective head-based sampling, combined with the addition of tags for estimation and historical accuracy, and the integration of metrics alongside spans, provided better control over costs and improved the accuracy of observability for application teams. Despite achieving significant reductions, Capital One continues to refine their approach, focusing on dynamically adapting tail-sampling processors to balance the needs of high-frequency and low-frequency applications.
Why It Matters
A technical IT operations leader should read this article because it provides a practical, real-world case study of how a large enterprise like Capital One tackled a common and costly problem: telemetry data overload. The article highlights the limitations of traditional vendor tools and head-based sampling, advocating for OpenTelemetry and tail-based sampling as a more effective solution. It offers actionable insights into best practices, such as using tags for data estimation and historical accuracy, and integrating metrics with spans for a comprehensive system view. Furthermore, it demonstrates how a strategic shift in observability practices can lead to significant cost savings (70% data reduction) while simultaneously improving the quality and reliability of operational insights, which is crucial for optimizing operations, debugging, and preventing outages in complex IT environments.
