IDC estimates that over the next two years, 90% of new enterprise applications will be developed as cloud-native apps, using agile methodologies and API-driven architectures that leverage dynamic microservices, containers, and serverless functions. These trends are accelerating faster than IT teams can keep up, which creates a constant churn of complexity that has made observability at scale all but impossible to achieve manually.
Manually is the operative word here. As organizations continue to ramp up their digital transformation efforts, the pace of software innovation through increased investment in cloud-native development – using containers, microservices, and open-source standards like OpenTelemetry – is only going to get faster. Dynamic environments that use Kubernetes, serverless, and multicloud architectures will only get more complex.
Managing this speed and complexity requires automated, AI-assisted observability into your full multicloud environment. That observability depends on next-generation distributed tracing capabilities – tracing designed for cloud-native, multicloud environments – that are capable of not only supporting open standards like OpenTelemetry, but also complementing and enriching them with code-level context.
Here are three reasons observability of complex multicloud environments can’t work without an automated, next-gen approach to distributed tracing.
1. Conventional distributed tracing is limited
With conventional distributed tracing, when a user interacts with an application, the transaction is assigned a unique ID. That ID travels with the transaction as it makes other requests to additional apps, microservices, and infrastructure, tracking dependencies between services along the way.
But methods that rely only on metrics, logs, and single traces miss a lot of context – details like code and metadata, context like user behavior, and data from open-source standards that developers and analysts can use to debug and manage services. Conventional methods might also fail to provide traces across cloud boundaries, which is a major blind spot for hybrid-cloud and multicloud environments that depend on observability across public and private clouds. Simply put, conventional distributed tracing approaches can leave IT teams short of a complete and insightful picture of their cloud and container-based environments.
2. Open source doesn’t automatically mean higher quality data for tracing
Open-source solutions like service mesh, OpenTelemetry, and serverless applications may open new data sources for observability and managing Kubernetes, but that data does not extend to interfaces with other services. This limitation leads to incomplete traces, gaps, and blind spots, and often requires teams to make multiple manual code changes just to yield what is still low-resolution data.
OpenTelemetry data, for example, does not include additional relevant information like container metrics, infrastructure health metrics, or code-level details like method hotspots and CPU analysis. OpenTelemetry and other open-source tools are valuable, but to overcome incomplete traces, gaps, and blind spots, they need to be complemented with enriched data to provide full context for IT teams.
3. Manual distributed tracing adds more work and complexity for already stretched IT teams
Distributed tracing promises to streamline ITOps and make it easier to determine dependencies and pinpoint anomalies. But if the traces are incomplete, lack full context, or are burdened by low-resolution data from open-source tools, teams still have to spend time chasing down which traces or parts of a trace need to be analyzed manually.
By relying solely on metrics, logs, and single traces, the conventional approach to distributed tracing means teams have to spend time manually instrumenting telemetry code, reconfiguring instrumentation after updates, and continually optimizing data when they could be spending time innovating to deliver better business outcomes.
Next-generation distributed tracing takes a whole new approach
Distributed tracing for complex multicloud environments needs to leap forward into a new generation, powered by continuous automation and AI-assistance.
Next-generation distributed tracing means automatically discovering and picking up telemetry instrumentation from all monitored hosts, services, processes, and applications – including instrumentation from OpenTelemetry, serverless apps, service mesh, and other open-source services. It means providing code-level insights, like an exact method hotspot or CPU time to a central observability platform for debugging and performance tuning. It means shipping updated instrumentation code without requiring teams to update or redeploy their applications.
Next-generation distributed tracing also means taking an AI-driven approach to evaluating all telemetry data in real time and providing relevant and precise answers – not statistical guesses – that detail the current state of your multicloud environment.
AI-assistance and continuous automation go hand-in-hand with next-gen distributed tracing to transform the way IT teams work. With the ability to pinpoint and remedy system anomalies and analyze user behavior and performance issues from a single source of truth, teams can innovate faster and collaborate more efficiently.
Distributed tracing for a new age of intelligent observability
Multicloud environments with dynamic applications, containers, and microservices are only going to get more complex, not less. The conventional approach to distributed tracing simply doesn’t scale.
Next-generation distributed tracing across the full stack is about delivering automatic and intelligent observability at scale that generates precise code-level insights and real, actionable analysis. It’s the only way IT teams can achieve the observability they need at scale to not just keep up with complexity, but push the pace of software innovation and improve digital experiences that drive better business outcomes.
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