What are the Challenges of Observability?
- Data Silos: Understanding the interdependencies across applications clouds and digital channels like web, mobile and IoT is difficult due to multiple agents, different sources of data, siloed monitoring tools.
- Data Overload: It is almost impossible to make sense of the vast volumes of raw data collected from all components in dynamic cloud environments such as AWS, Azure and Google Cloud Platform (GCP) as well as Kubernetes and containers.
- Manual Instrumentation: IT resources dedicate the majority of their time to establishing observability rather than acting on insights when they need to manually instrument and modify code for every new component or agent.
- Lack of Pre-Production: Developers cannot see how actual users will impact applications and infrastructure before pushing code into production even with load testing.
- Troubleshooting Inefficiency: Telemetry data across multiple tools and vendors makes no sense; teams lose valuable time trying to identify root causes for problems.
- Multiple Tools: There are many systems that could impact performance so one single tool might not give full observability across all application systems.
What is Observability?
As technology systems become more complicated, the teams that manage them face growing challenges in keeping track of and addressing problems across different cloud environments. Due to this, the teams responsible for operations, development, and system reliability seek better visibility and understanding of these diverse and intricate computing setups. They need simpler ways to monitor and identify the issues within these complex systems.