An introduction to converting logs into metrics using OpenObserve. It explains the differences between logs and metrics, why logs alone don’t scale efficiently, and demonstrates how to build pipelines that transform log data into actionable metrics for dashboards and alerts
The difference between logs and metrics and their respective use cases
Why converting logs to metrics improves scalability and efficiency
Common challenges when handling logs at scale
How OpenObserve pipelines work for data transformation
Step-by-step process to convert logs into metrics
How to set up and schedule pipelines in OpenObserve
Techniques for debugging and resolving pipeline errors
How to prepare data for dashboards and alerting systems
This video walks through the process of converting logs into metrics using OpenObserve, starting with a clear explanation of what logs and metrics are and how they differ in purpose. It highlights the limitations of relying solely on logs, especially in large-scale environments where performance and cost become concerns.
The tutorial then introduces OpenObserve and its pipeline capabilities, showing how data can be efficiently aggregated and transformed. Using a Kubernetes log stream as a practical example, it provides a step-by-step guide to setting up a scheduled pipeline, converting logs into a metrics stream, and handling errors during the process.
By the end of the video, you will understand how to create a clean, efficient metrics stream for dashboards and alerts while still preserving detailed log data. The video also encourages viewers to explore OpenObserve further through its free cloud trial and community resources.
Simran specializes in DevOps, cloud-native technologies, and observability, with hands-on experience in Kubernetes, Docker, and AWS. Creates practical, accessible technical content and solutions that help teams simplify complex workflows and improve system reliability.
