2. Build out the data

The telemetry data that an observability platform collects can be divided into several types:

Metrics - A numerical assessment of application performance, resource utilization, and overall system health over a given period of time.

Traces - A record of the end-to-end services for every user request, as transactions move from one service to another.

Dependencies – An assessment of how each application component is dependent on other components, applications and IT resources.

Health Checks – Periodic polls of specific services. If a health check fails, it turns into an issue.

Alerts – Notifications triggered when specific predetermined thresholds are exceeded.

Dashboards – Application perspectives that provide visual, interactive, and understandable presentations of specific predetermined metrics.

Logs - Granular, timestamped, complete and immutable records of application events within your system.

After gathering this information, observability tools correlate it in real-time to provide contextual information — the what, where and why of any event that could indicate, cause or be used to address an application performance issue.

The most advanced observability platforms will also automatically discover new sources of telemetry that might emerge from the application. Because these platforms handle so much more real-time data than a standard APM solution, many include AIOps (artificial intelligence for operations) and ML (machine learning) capabilities that can separate the signals (indications of real problems) from the noise (data unrelated to problematic issues).

3. Put key practices and principles into place

In order to work properly, observability relies on data and AI to fuel automated, actionable insights. Those key components let you effectively manage and observe a multitude of services and systems across complex environments at speed and scale. Thus, there are several key practices and principles that you need to have in place in order for your observability platform to achieve your objectives:

Systematic optimization: Observability maps and contextualizes interactions between all the resources that exist within the IT architecture so that the entire IT environment can be optimized.

Complete contextualization: Every unit of observability data must be delivered with complete context. End-to-end tracing and contextualization of every metric is required.

Cloud-native deployment: Observability deployment and instrumentation processes are fully automated so that observability tools integrate seamlessly into cloud-native application environments.

Comprehensive support for data ingestion: Observability supports the many ways that modern application environments expose data – ranging from standard output and conventional logs to telemetry such as OpenTracing.

Observability across the pipeline: Shift-left observability uses observability platforms in preproduction for optimizing the CI/CD pipeline and finding issues so that they aren’t released into production.