Dynatrace has announced an enhancement of its infrastructure monitoring capabilities, paving the way for users to search and analyse logs from Kubernetes and multicloud environments.
The announcement means DevOps and site reliability engineering teams (SREs) will be better-placed to easily search and analyse real-time and historical logs from any source, all in a centralised location, without log-targeting or manual intervention.
Users can use Dynatrace to combine this log data to further simplify cloud complexity at scale. The software intelligence company combines log data with observability and user experience data to provide AI-driven answers with root-cause for faster problem remediation.
“We are continuously simplifying cloud complexity by bringing automation and AI-assistance to new data sources as they become available,” says Dynatrace senior vice president of product management Steve Tack.
“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.”
The volume, velocity and variety of data is proliferating rapidly in today's IT environment. Legacy monitoring and DIY approaches can only do so much, and often shift the burden of making sense of data solely onto digital teams.
Dynatrace's new enhancements make it easier to understand and react to this data. Here's how:
Expanded log ingest and storage
This includes 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.
A new Dynatrace Log Viewer
This provides users with powerful filtering capabilities to empower teams to search, analyse, and segment real-time and historical log data from any source in a centralised location. Teams can easily explore logs across multicloud environments and analyse them in the context of their architecture.
This feature 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
Davis detects 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,” says Experian global chief enterprise architect Mervyn Lally.
“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.”
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