Observability is the ability to infer what is happening on a computing platform by monitoring and analyzing the outputs of that platform. This is important for areas such as workload performance monitoring and platform security.

Using observability means there is no need for very granular knowledge of the underlying physical platform, which is useful with today’s hybrid private and public systems. But several areas need to be covered to ensure you can trust what the results are telling you.

1. Know your platform.

This goes against the idea that observability does not require granular knowledge of the physical platform, but without this knowledge it is difficult to identify all possible sources for data streams. As such, a discovery engine is necessary to carry out an audit of the platform. Many of these streams will be tied to virtual environments, so you don’t need to identify the specific physical hardware they are connected to. A good discovery engine will keep everything updated as new resources are added or removed from the platform.

2. Enable data logging where it is not already enabled.

Use Simple Network Management Protocol or other means to create a standardized data record whenever possible. When proprietary data formats are used, ensure that they are accessible. Use connectors that can translate data into a standardized form; many of the data aggregation tools mentioned below will have this capability either out of the box or as add-ons.

3. Filter the data as close to the creation point as possible.

Much of the data created by a computing platform will be of no use – it basically means that everything is fine. An observability system should be designed to filter data at multiple levels to ensure that bandwidth is not swamped by excessive chatter and that data analysis can be performed quickly and efficiently in real time. But be careful: filtering out what seems unimportant to the operations team can be very important when aggregated with data from other sources.

4. Ensure data can be aggregated and centralized.

Observability requires a way to analyze data to recognize patterns and anomalies so the platform can report what it sees. Systems such as Splunk, Datadog, and LogDNA have shown how data can be centralized and used to provide observability insights.

5. Data analysis tools should be fit for purpose.

Analytics tools that don’t catch key areas, such as early-stage issues or zero-day attacks on the platform, won’t provide the peace of mind that an observability system provides. efficient. Most observability approaches revolve around systems such as security intelligence and event management products such as LogRhythm, FireEye, or Sumo Logic.

These products, built on organizations’ need to secure their platforms against internal and external threats, are quickly recognizing that they have the capabilities to become observability offerings and can use their pattern recognition and advanced heuristics to identify other issues, such as problems at a virtual or physical level on a computing platform.

6. Report in the right way.

Observability should not be seen as a tool for system administrators or DevOps practitioners only, but as a way to bridge the gap between IT and the business by reporting what it sees and providing guidance on what it sees. which needs to be done. Reports should inform IT professionals in real time of current issues and provide trend analysis and business impact reports understandable to line of business personnel.

7. Integrate automated remediation systems where possible.

Many issues identified by an observability offering will be relatively low level. Most system administrators already have tools to automatically resolve issues such as systems needing patches or updates, or when additional resources need to be applied to a workload. By integrating an observability system into these tools, IT can more easily maintain an optimized environment. Where automation is not possible, such a filter allows IT to focus on the most important issues and resolve them faster.

8. Feedback loops must be present and effective.

Repeated identification of security issues or resource issues may be caused by coding or implementation issues that cannot be resolved by automated means. Linking observability systems to support and trouble ticket management offerings ensures that areas are selected and assigned to the right IT staff.

Observability is becoming a necessity as organizations move to a more decentralized computing platform. Without the ability to aggregate and analyze data from all areas of an IT platform, organizations face issues ranging from inadequate application performance to poor user experience to security issues. majors. In the long term, observability will differentiate the performance of organizations in a highly dynamic and complex world.