Observability Pipeline: What It Is & How to Build One

Observability pipelines are automated workflows that enhance system monitoring by collecting, transforming, and storing observability data, which includes metrics, logs, and traces from various sources. These pipelines address challenges in managing large volumes of data by employing an ETL (Extract-Transform-Load) process to filter, normalize, and format data, ensuring efficient analysis and improved insights. By standardizing data processing and reducing noise, observability pipelines help organizations optimize data control, reduce costs, and enhance performance insights while maintaining security and compliance. Tools like OpenTelemetry Collector, Fluentd, and Elasticsearch are commonly used to implement these pipelines, which are designed to be scalable and vendor-agnostic to prevent lock-in and facilitate integration with multiple cloud services. Best practices include defining clear data collection goals, focusing on actionable insights, and maintaining simple transformations to improve performance and scalability. Platforms like Spacelift further support these workflows by enabling orchestration of infrastructure tooling and offering integrations with observability tools such as Prometheus and Datadog for precise monitoring and automation.