Modern approaches to observability and monitoring for multicloud environments

A modern observability solution can transform data from across complex, distributed environments into actionable business intelligence.

IT, DevOps, and SRE teams are racing to keep up with the ever-expanding complexity of modern cloud ecosystems and the business demands they are designed to support. Teams are weighing the differences between observability and monitoring as they seek solutions.

Leaders in tech are calling for radical change. A 2020 Global Survey found that 56% of CIOs think their IT teams can never complete everything the business needs, and 70% agree their teams waste precious time on manual tasks that could be automated if only they had the means.

These increasingly complex public, private, and distributed multi-cloud environments require a modern approach to observability that incorporates continuous automation and AI-assistance to gain actionable insights quickly.

Observability and monitoring: Why observability brings multicloud environments to heel

Observability is the new standard of visibility and monitoring for cloud-native architectures. It’s powered by vast amounts of collected telemetry data such as metrics, logs, events, and distributed traces to measure the health of application performance and behavior. It can empower teams to identify the effect of an incident quickly and pinpoint the cause of the specific behavior or event. Observability and monitoring help developers understand not only what’s wrong in a system — what’s slow or broken — but also why an issue occurred, where it originated, and what impact it will have.

According to Nancy Gohring, a senior analyst at 451 Research, “The key to achieving observability is collecting the right data set and making sure you can flexibly dig into that data to discover what’s happening.”

Making systems observable gives developers and DevOps teams visibility and insight into their applications, as well as context to the infrastructure, platforms, and client-side experiences those applications support and depend on. With this information they can:

• Detect outages, software bugs, unauthorized activity, and service degradations.
• Report on the health of the system by measuring performance and resources.
• Understand how neighboring or dependent services might impact each other.
• Find unknown unknowns that have never occurred in the past.
• Identify long-term trends for capacity planning and business objectives.

Requirements to achieve multicloud observability and monitoring

Environments with multiple cloud service providers that deploy microservices, containers, and Kubernetes systems require a more dynamic, modern approach to monitoring. The level of complexity in these systems surpasses the ability of traditional monitoring tools to measure, and performance issues are increasingly difficult to pinpoint. For example, DevOps teams cannot preconfigure a separate dashboard to monitor every metric for a single application system because it has become impossible to predict in advance what problems are most likely to occur.

Yesterday’s monolithic processes used preallocated resources set aside for a specific purpose, provisioned for a specific need. These have been replaced by agile, portable microservices that get spun up as they are needed. Observability in distributed environments — with countless application components and dependencies — requires monitoring a growing volume and variety of data and the ability to scale dynamically on the back end to collect and analyze this data.

Another challenge is overcoming alert storms. When hundreds to thousands of alerts come in at once, it is nearly impossible for teams to establish which ones are relevant. To identify those that matter most and make them visible to the relevant teams requires a modern observability platform with automation and artificial intelligence (AI) at the core.
Key capabilities of the best observability solutions include:

• AI-powered insights — Evaluate millions to billions of data points automatically in seconds with causation-based AI that uses real-time statistical models and baselining to pinpoint root cause with code-level precision.
• Automatic full-stack discovery — Capture every component in the environment from top to bottom, including all the interconnected relationships and dependencies, with no manual configuration or coding, to gain real-time contextual awareness.
• Distributed traces across open source and cloud-native architectures — Analyze transactions end-to-end across cloud-native technologies, including OpenTelemetry, W3C Trace Context, service mesh, and serverless computing to eliminate gaps, blind spots, and incomplete traces

Turning raw data into actionable business intelligence

With the help of AI-assisted topology mapping, data that has been collected, observed, and analyzed becomes actionable in real-time. This capability provides the context needed to make sense of data no matter where in the entire technology stack it originated. It automatically detects and collects a rich set of metadata, capturing the relationships between all system components to reveal the dependencies between each app and service.

With traditional monitoring tools, metrics, logs, traces, and user experience data is stored in data silos without context that ties them together to provide meaning. However, an automatic and intelligent observability solution identifies the impact to users at the time of an incident, so you can prioritize the most critical issues and drive better business outcomes.

AI-driven software intelligence for observability and monitoring

IT teams operating multicloud ecosystems need continuous full-stack observability with AI-assisted automation to identify, fix, and prevent performance issues in real-time. The Dynatrace Software Intelligence Platform, and its powerful AI engine Davis®, automate root-cause analysis and discover the unknown unknowns, all without missing a beat in today’s most complex cloud-native architectures.