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Dynatrace is enhancing its infrastructure monitoring capabilities to search and analyze logs from Kubernetes and multicloud environments, as well as the most widely used open-source log data frameworks. These enhancements will allow DevOps and Site Reliability Engineering teams (SREs) to easily search, segment, and analyze real-time and historical logs from any source, all in a centralized location, without log-targeting or manual intervention. To further simplify cloud complexity at scale, Dynatrace® combines this log data with extensive observability and user experience data to provide AI-driven answers with root-cause for faster problem identification and remediation, and maximum business impact.
“We are continuously simplifying cloud complexity by bringing automation and AI-assistance to new data sources as they become available,” said Steve Tack, SVP of Product Management at Dynatrace. “We provide the widest and deepest observability coverage, while simultaneously providing the advanced analytics to make digital teams, in this case DevOps and SREs, smarter and able to cover more ground by automating away complexity and wasted motions.”
Legacy monitoring, observability-only, and do-it-yourself approaches leave it up to digital teams to target, capture, and make sense of their data. With the volume, velocity, and variety of data exploding, understanding, and reacting to this data is harder and more time consuming than ever. Dynatrace addresses these challenges with:
· Expanded log ingest and storage, including logs from Kubernetes and multicloud environments, Amazon Web Services, Google Cloud Platform, Microsoft Azure, and Red Hat OpenShift, as well as the most widely used open-source log data frameworks, such as Fluentd and Logstash.
· New Dynatrace Log Viewer, providing powerful filtering capabilities to empower teams to search, analyze, and segment real-time and historical log data from any source in a centralized location. Teams can easily explore logs across multicloud environments and analyze them in the context of their architecture.
· Dynatrace Smartscape® continuously maps cloud log data with the extensive observability data it already collects, reflecting the technologies and dependencies in multicloud environments, as well as users’ experiences with these technologies.
· Dynatrace’s AI engine, Davis® provides precise, real-time answers, detecting anomalies based on log events and other data, and automatically identifies the root cause of infrastructure problems such as Kubernetes service degradations, saving DevOps and SREs more time for innovation.
“With Dynatrace automatically collecting log data from Kubernetes and multicloud environments, as well as metrics from open data frameworks, we have simplified the management of our complex, multicloud IT environment,” said Mervyn Lally, Global Chief Enterprise Architect at Experian. “Combining this data with the traces, UX, and other data already captured by Dynatrace, and applying its powerful automation and AIOps capabilities, enhances cross-team collaboration between our applications and infrastructure teams, and empowers them to deliver better user experiences.”