What does the term 'full-stack observability' actually mean? And how does it work? And last but not least: What advantages does the technology offer in terms of cyber security and the automation of manual processes?

See every end point

'Full-stack observability' describes the ability to determine the state of each endpoint in a distributed IT environment based on its telemetry data. Endpoints include local servers, Kubernetes infrastructures, cloud-hosted infrastructures and services, and open source technologies. Observability of the entire technology stack provides IT teams with comprehensive, real-time insight into the behavior, performance and health of applications and the underlying infrastructure.

Observability solutions use telemetry data such as logs, metrics and traces to provide IT managers with insights into application and infrastructure performance as well as the actual user experience. This context also makes it possible to understand how all these entities are connected to each other. This is not just about infrastructure connections, but also about the relationships and dependencies between containers, microservices and code at all network layers.

Last but not least, comprehensive observability is essential for digital experience monitoring (DEM). This allows the basic user experience to be determined. Specialists can optimize website load times, web application response times for end users and reduce latency. The rapid resolution of problems ensures a positive customer experience and an undisturbed shopping experience for web stores, for example.

Observability of the entire system

Cloud environments present IT with much more complex challenges than traditional, local data centers. In response, many companies are adding more monitoring tools. However, the proliferation of tools can add to the complexity and even slow down the response to issues due to a lack of visibility. When IT teams use their tools in separate silos, they lack a complete picture of activity. Without a centralized source where all relevant information is brought together, even the best professionals often struggle to understand and efficiently resolve urgent business-critical issues.

Observability changes this. It provides visibility into every layer of the application infrastructure through a centralized platform, reducing the proliferation of monitoring tools that comes with high costs and inefficiencies. DevOps teams particularly benefit from the observability of the full technology stack. They receive accurate, real-time feedback from integration or production systems, enabling them to resolve user experience and application performance issues faster. Thanks to improved diagnostics and analytics, DevOps teams spend less time troubleshooting and can instead use their expertise to develop new features that benefit users.

Six advantages of full-stack observability

Awareness of the entire environment: Many companies have a multi-cloud environment. Observability provides end-to-end insight into the entirety of the application environment and eliminates blind spots in terms of performance, health and behavior of all applications. Without complete observability, professionals have at best a partial understanding of their application infrastructure.

Precise root cause identification and prioritization of problems: IT and Dev Ops teams don't waste time sifting through data and interpreting statistics. With Observability, they can quickly identify root causes and prioritize issues based on user and business impact.

Accelerate and automate the CI/CD pipeline: Full-Stack Observability helps DevOps teams quickly identify potential issues in the CI/CD (Continuous Integration and Continuous Delivery) pipeline and fix them faster. This means that new software can be put into production faster, companies increase their innovation speed and benefit from the implementation of good ideas more quickly.
Integration of runtime application security in DevSecOps: Security teams need to address and manage risks. This is why they are often perceived by other IT teams as a brake on innovation. Full-Stack Observability eliminates this internal conflict by enabling IT teams to more effectively integrate runtime application security into DevSecOps processes. This allows IT staff to support business objectives without compromising security.

Better business decisions with accurate analytics: When IT teams and business departments don't work hand-in-hand, it's difficult for organizations to make business-critical decisions. Observability provides the transparency needed for all stakeholders to have a common frame of reference and agree on business priorities.

Eliminating operational silos and optimizing collaboration: With a centralized source of all relevant information for root cause analysis, IT and DevOps teams can quickly agree on what needs to be done to ensure good function and who is responsible for it. This improves cross-departmental working relationships and ensures fluid internal collaboration.

AI-supported observability

The complexity of the cloud has made it difficult to gain insight into the entire system. But with a centralized platform that automates monitoring across the entire technology stack, companies can get all the operational and business performance metrics they need - from the front end to the back end and everything in between.

The author: Andreas Grabner is CNCF Ambassador and Dynatrace DevRel.

© Dynatrace

Artificial intelligence also works wonders here: as soon as a system brings together all the necessary telemetry data, deterministic AI can quickly show what is important and why. This makes it easy for specialists to take the right measures. The use of AI for IT operations (AIOps) not only helps to master the flood of information in IT operations, but also to increase efficiency and the speed of innovation. AIOps enable automated root cause analysis.

Thanks to the observability of the entire system, companies can see through everything - from the end user's experience to the state of the infrastructure, including all relationships and dependencies between the individual layers, components or pieces of code.