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Observability
“DevOps is the union of people, process, and products to enable CONTINUOUS DELIVERY of value to our end users.” – Donovan Brown of Microsoft
Return to Continuous Monitoring (CM), Monitoring topics, Continuous Integration (CI) / Continuous Delivery (CD) = CI/CD, Continuous integration (CI), Continuous deployment (CD) - CI/CD
TLDR: Observability refers to the ability to measure and analyze the internal state of a system based on the telemetry data it produces, such as logs, metrics, and traces. Popularized in the 2010s with the rise of microservices and distributed architectures, observability ensures system reliability and helps teams identify, diagnose, and resolve issues efficiently.
https://en.wikipedia.org/wiki/Observability_(software)
The three pillars of observability—logs, metrics, and traces—are key to understanding system behavior. Logs provide detailed event data, metrics capture system performance trends, and traces show how requests propagate through distributed systems. Tools like Grafana, Prometheus, and Jaeger enable teams to collect and analyze this data, helping to pinpoint issues like latency spikes or failed transactions.
Observability is critical for modern DevOps and SRE practices. By integrating frameworks like OpenTelemetry, teams can gather telemetry data across diverse environments, from cloud-native applications to on-premises systems. This data is then processed by observability platforms like Datadog or Elastic Stack to visualize and monitor application health, enabling proactive issue management.
Effective observability improves system reliability, reduces downtime, and enhances user satisfaction. It allows for real-time monitoring and analysis, enabling teams to correlate events and identify root causes quickly. As systems become more complex, observability ensures that organizations maintain control and adaptability in dynamic environments.
https://www.datadoghq.com/what-is-observability/
- Snippet from Wikipedia: Observability
Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. In control theory, the observability and controllability of a linear system are mathematical duals.
The concept of observability was introduced by the Hungarian-American engineer Rudolf E. Kálmán for linear dynamic systems. A dynamical system designed to estimate the state of a system from measurements of the outputs is called a state observer for that system, such as Kalman filters.
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