TABLE OF CONTENTS:
- What's inside
- From observability to getting answers
- Modern cloud environments need a radically different approach to observability
- Extending traditional observability for actionable answers
- Automation for scalability and completeness
- Real-time topology mapping provides context across the full stack
- Causation-based AI delivers precise answers
- Looking ahead: OpenTelemetry for better coverage
Observability, when combined with AI and automation, holds the promise to deliver the actionable answers needed to ensure cloud-native applications work perfectly and deliver the best experience and value possible to their users.
We recognized that while observability is important, it’s not enough to just “observe” data– it’s important to use data to deliver better business outcomes. As microservice environments become highly dynamic and grow to thousands of hosts, the real challenge becomes making sense of data and deriving answers toperformance problems in real time. This can be a daunting task that quickly surpasses the capacity of human operators.
That’s why Dynatrace developed a radically different Software Intelligence Platform, expanding traditional observability with automated, AI-powered answers that scales across hundreds of thousands of hosts. This platform is used today by many of the word’s largest enterprises.
*In software, observability refers to the extent that the internal status and performance of a system can be inferred from its externally available data.
Extend traditional observability with actionable answers
Observability addresses the challenges of cloud-native applications by proposing a better way of collecting data from all system components to gain complete and seamless visibility. Most conventional tools focus on collecting three principal data types—metrics, logs, and traces—the so-called three pillars of observability.
Dynatrace has pioneered and expanded the collection of observability data in highly dynamic cloud environments with the OneAgent. In addition to metrics, logs and traces, we are also collecting user experience data for full, end-to-end visibility.
Most importantly, Dynatrace delivers answers, not just more data, through three distinct capabilities:
Automatic discovery and instrumentation
Ensure scalability and complete coverage in highly dynamic environments without manual configuration.
Understand the interdependencies between different entities and the data being observed, in context and across the full stack.
Causation-based AI engine
Provide actionable answers to performance problems through a precise root-cause analysis.
Automation provides scalability and completeness
Most observability approaches require developers to manually instrument their code. In environments with tens of thousands of hosts and microservices that dynamically scale across global, multi-cloud infrastructure, this becomes a futile effort.
The Dynatrace platform automates data collection and analysis for enterprise-grade scalability and complete observability.
Upon installation, the Dynatrace OneAgent automatically and instantly detects all applications, containers, services, processes, and infrastructure at start-up time.
System components are instrumented automatically with zero configuration or code change. Collection of high-fidelity data such as metrics, logs, traces, and user experience, in addition to topology data, start as soon as a system component becomes available.
Dynatrace’s smart baselining automatically learns “normal” performance and adapts dynamically as the environment changes.
For enterprise-grade maintainability, the OneAgent automatically and securely updates throughout the entire environment.
- Built at the core of the Dynatrace platform Davis processes all observability data across the full technology stack, independent of origin.
- Precise technical root-cause analysis. Davis pinpoints malfunctioning components by probing billions of dependencies in milliseconds.
- Identification of bad deployments. Davis knows exactly what deployment or config change has introduced the anomaly in the first place.
- Discovery of unknown unknowns. Davis does not rely on predefined anomaly thresholds but automatically detects any unusual “change points” in the data.
- Automatic hypothesis testing by systematically working through the complete fault tree.
- No repetitive model learning or guessing. Unlike machine learning approaches, Davis’ causation-based AI relies on a topology map, which is updated in real-time.
Looking ahead: OpenTelemetry for better coverage
The OpenTelemetry open-source project is spearheaded by the Cloud Native Computing Foundation (CNCF), with the aim of making software more observable and to establish telemetry as a built-in feature of cloud-native software. OpenTelemetry focuses on improving the collection of observability data, specifically metrics, and distributed traces for some of the emerging and increasingly adopted cloud frameworks.
This initiative is broadly supported by the open source community, as well as leading contributors including Dynatrace, Google, and Microsoft. Dynatrace is actively contributing and sharing its expertise with auto-instrumentation, interoperability, and enterprise grade solutions. Once OpenTelemetry is more widely adopted as a standard, it will serve as an additional data source that further extends the breadth of Dynatrace’s technology coverage.
The Dynatrace platform will help enterprises leverage OpenTelemetry by providing the highest possible scalability through automation, full-stack topology mapping, and most importantly, causation-based analytics through our AI engine, Davis, to deliver answers, not just more data.