Source: medium.com
The Practice of Chaos Engineering ObservabilityCategory: Business, Data, Kubernetes, Infrastructure, automation
Today’s modern distributed systems are associated with many unpredictable failure scenarios that are extremely difficult to monitor across all failure points.
However, monitoring itself is not good enough when we are dealing with modern systems associated with complex integration points and interfaces across operating systems, Kubernetes layers and application stacks.
Observability is the property of the system that helps understanding what is going on in the system and getting related information to troubleshoot.
The practice of chaos engineering observability will improve confidence in the system, enable faster deployments, prioritize business KPIs and drive auto healing of systems. Use of AI/ML will aid in building observability patterns and antipatterns by close monitoring of system and user behaviour over a period of time.
However, monitoring itself is not good enough when we are dealing with modern systems associated with complex integration points and interfaces across operating systems, Kubernetes layers and application stacks.
Observability is the property of the system that helps understanding what is going on in the system and getting related information to troubleshoot.
The practice of chaos engineering observability will improve confidence in the system, enable faster deployments, prioritize business KPIs and drive auto healing of systems. Use of AI/ML will aid in building observability patterns and antipatterns by close monitoring of system and user behaviour over a period of time.
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