Enhancements to workload and platform analytics drive performance optimization for containerized applications and microservices at scale
WALTHAM, Mass. & LAS VEGAS--(BUSINESS WIRE)--Software intelligence company, Dynatrace (NYSE: DT), today announced new enhancements to its support for Kubernetes at the Perform 2020 conference. The Dynatrace® explainable AI engine, DavisTM, now automatically ingests additional Kubernetes events and metrics, enabling it to deliver precise answers in real time about performance issues and anomalies across the full stack of Kubernetes clusters, containers and workloads. Dynatrace now also automatically discovers, instruments and maps heterogeneous container technologies within Kubernetes, making even the largest and most diverse containerized environments easier to deploy and manage, thereby simplifying IT’s journey to cloud-native environments.
According to a recent CIO research report, 68% of organizations are already using containers, with 86% expecting to have deployed them within 12 months. Kubernetes is becoming a preferred container orchestration system. As organizations digitally transform and scale up their Kubernetes applications and microservices in production, the dynamic and distributed nature of these environments make it impossible to understand and manage simply by looking at metrics and dashboards. Full-stack observability, coupled with precise AI-powered answers are needed across the Kubernetes platform, containers and workloads.
“We reinvented Dynatrace specifically to support multi-cloud and microservices architectures, providing precise answers about the applications and infrastructure in these environments in real time and at scale,” said Steve Tack, SVP of Product Management at Dynatrace. “Our explainable AI engine, Davis, now automatically ingests additional Kubernetes metrics, making Dynatrace even smarter about clusters and containers. This distinguishes us as the only observability solution that provides precise answers about issues and anomalies across the full stack, from the platform to the workloads running on it. Ultimately, that’s what enables our customers to tackle the complexity inherent in Kubernetes environments with speed and efficiency.”
“In a dynamic cloud environment, it’s crucial that we have full-stack observability so we can continually understand and optimize performance,” said Takuya Tonaru, Product Owner at Yahoo! Japan. “Dynatrace allows us to do exactly that, providing both deep visibility into resource utilization and performance automatically, as well as identifying issues and anomalies across our entire web-scale cloud via its AI engine, Davis. This allows us to spend our time focused on building a better experience for our customers rather than gathering facts from disconnected tools to gain performance degradation insights.”
Key enhancements to the Dynatrace® platform’s support for Kubernetes environments include:
- Precise, AI-powered answers – DavisTM has been enriched with the ability to ingest additional Kubernetes events and metrics, including state changes, workload changes and critical events across clusters, containers and runtimes. As a result, Dynatrace better understands all dependencies and relationships across the entire Kubernetes stack, from clusters to containers, and the workloads running inside. This further enables Dynatrace to provide full-stack observability at scale, and deliver more precise, AI-powered answers to dramatically simplify Kubernetes roll-out and management.
- New cloud application and microservice analysis capabilities – With Dynatrace, organizations can now understand and optimize Kubernetes resource utilization, enabling administrators and application owners to identify and solve performance issues and improve business outcomes proactively.
- Extended automatic container instrumentation – Dynatrace now automatically discovers, instruments and maps heterogeneous container technologies within Kubernetes environments, including implementations based on Docker, CRI-O and containers. This makes it easy to deploy and manage even the largest containerized environments. New container resource usage analysis also provides broader coverage for the range of container runtimes used by organizations.
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