Hybrid cloud is the norm
Enterprises are rapidly adopting cloud infrastructure as a service (IaaS), platform as a service (PaaS), and function as a service (FaaS) to increase agility and accelerate innovation. Widespread cloud adoption has made hybrid cloud the norm. According to RightScale, 81% of enterprises currently execute a cloud strategy. Meanwhile, 451 Research predicts that more than 66% of enterprises will operate a hybrid cloud environment by 2019.
The result of hybrid cloud is bimodal IT—the practice of building and running two distinctly different application and infrastructure environments. Enterprises must continue to enhance and maintain existing, relatively static environments while also building and running new applications on scalable, dynamic, software-defined infrastructure in the cloud.
Simplicity and cost savings drove early cloud adoption, but today, cloud use has evolved to a complex and dynamic landscape.
The ability to seamlessly monitor the full technology stack across clouds, while also monitoring traditional on-premise technology stacks is critical to automating operations — no matter what the distribution level of the applications and infrastructure being monitored.
As enterprises migrate applications to the cloud, or build new cloud-native applications, they also maintain traditional applications and infrastructure. Over time, this balance will shift from the traditional tech stack to the new stack, but both new and old will continue to coexist and interact.
Microservices and containers introduce agility
Microservices and containers are revolutionizing the way applications are built and deployed, providing tremendous benefits in terms of speed, agility, and scale. In fact, 98% of enterprise development teams expect microservices to become their default architecture—and IDC predicts that 90% of all apps will feature microservices architectures by 2022.
According to 72% of CIOs, monitoring containerized microservices in real-time is almost impossible. Each container acts like a tiny server, multiplying the number of points you need to monitor. They live, scale, and die based on health and demand. As enterprises scale their environments from on-premise to cloud, the number of dependencies and data generated increase exponentially, making it impossible to understand the system as a whole.
The traditional approach to instrumenting applications involves the manual deployment of multiple agents. When environments consist of thousands of containers with orchestrated scaling, manual instrumentation becomes impossible and will severely limit your ability to innovate.
A manual approach to instrumenting, discovering, and monitoring microservices and containers will not work. For dynamic, scalable platforms, a fully automated approach to agent deployment, continuous discovery of containers and monitoring of the applications and services running within them is mandatory.
72% of CIOs say monitoring containerized microservices in real-time is almost impossible.
- Dynatrace CIO Complexity Report 2018
Not all AI is created equally. Attempting to enhance existing monitoring tools with AI, such as machine learning and anomaly-based AI, will provide limited value. AI needs to be inherent in all aspects of the monitoring platform and see everything in real-time, including the topology of the architecture, dependencies, and service flow. AI should also be able to ingest additional data sources for inclusion in the AI algorithms vs. correlating data via charts and graphs.
30% of IT organizations that fail to adopt AI will no longer be operationally viable by 2022.
Visualizing and prioritizing impact
Understand how specific issues or overall performance impacts every single user session or device and prioritize by magnitude.
Visibility from the edge to the core
A single view across your entire multi-cloud ecosystem. From the performance of users and edge devices to your applications and cloud platforms all in context.
A single source of truth for all
Ensure stakeholders, from IT to Marketing, have access to the same data to avoid silos, finger-pointing and war rooms.
Dynatrace worked out of the box. It’s tightly integrated with AWS, and it was almost a one-click process to enable insights across our AWS fleet. The entire rollout process took place within two days.”
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