The company's transition from fragmented observability tools to a unified system using OpenTelemetry and OneUptime dramatically improved incident response times, reducing MTTR from 41 to 9 minutes. By correlating logs, metrics, and traces through structured logging and intelligent sampling, they eliminated much of the noise and confusion that previously slowed root cause analysis. The shift also reduced the number of dashboards engineers needed to check per incident and significantly lowered the percentage of incidents with unknown causes.
Key practices included instrumenting once with OpenTelemetry, enforcing cardinality limits, and archiving raw data for future analysis. The move away from 100% trace capture and over-instrumentation helped manage data volume while maintaining visibility into anomalies. This transformation emphasized that effective observability isn't about collecting more data, but about designing correlated signals that support intentional diagnosis and reduce cognitive load.
Key practices included instrumenting once with OpenTelemetry, enforcing cardinality limits, and archiving raw data for future analysis. The move away from 100% trace capture and over-instrumentation helped manage data volume while maintaining visibility into anomalies. This transformation emphasized that effective observability isn't about collecting more data, but about designing correlated signals that support intentional diagnosis and reduce cognitive load.
A guide to building a robust logging system in Python, covering structured logging, log levels, handlers, formatters, filters, and integrating logging with modern observability practices.
This article compares three telemetry pipeline solutions – Cribl, Edge Delta, and DIY OpenTelemetry – based on scalability, performance, data management, intelligence, and cost. It details the strengths and weaknesses of each approach to help organizations choose the best solution for their observability and security data needs.
Traceloop's observability tool for LLM applications is now generally available. The company also announced a $6.1 million seed funding round. The platform extends OpenTelemetry to provide better observability for LLM applications, offering insights into model behavior and facilitating experimentation.
The article discusses the challenges faced due to differing observability tools and naming conventions, and how OpenTelemetry's standard naming schemas can streamline workflows and enhance interoperability.
Arize Phoenix is an open-source observability library for AI experimentation, evaluation, and troubleshooting, built by Arize AI.
This article provides an overview of OpenTelemetry, an open-source observability framework, and guides on integrating it with Go applications. It covers key concepts like logs, metrics, and traces, and demonstrates setting up a reusable telemetry package using OpenTelemetry in Go.
The article discusses the future of observability in 2025, highlighting the significant role of OpenTelemetry and AI in improving observability and reducing costs.
A reflection on the key achievements, contributions, and community efforts of 2024 for OpenTelemetry, including multilingual documentation and IA improvements.
Jaeger, a leading open-source distributed tracing platform, releases its version 2, which aligns with the OpenTelemetry framework, bringing improved performance, streamlined architecture, and native support for OTLP. The update includes advanced sampling techniques, expanded ecosystem access, and a more flexible storage implementation.
