Edge Delta Helps Overcome Challenges of Monitoring Kubernetes

Edge Delta, a supplier of an observability platform that examines entire datasets as they are generated at the source, has unveiled new capabilities that are intended to simplify logging and address the difficulties associated with monitoring Kubernetes systems. The Kubernetes Overview, Kubernetes Automated Findings, and Kubernetes Findings View tools from Edge Delta are ready to use out of the box without any additional configuration.

Photo Ozan Unlu, CEO and Founder of Edge Delta
“Some of the obstacles to monitoring Kubernetes environments stem from the very same traits that make it so attractive to organizations,” said Ozan Unlu, CEO and Founder of Edge Delta.

Many development teams would struggle to keep up with the frequent provisioning and deprovisioning of Kubernetes resources, making it difficult to continuously monitor their environments, according to Edge Delta. Additionally, Kubernetes environments would generate large amounts of data at high prices due to its distributed architecture.

Additionally, implementing logic at this level of granularity can be time-consuming, leaving developers unprepared and in a reactive position when a problem arises. Kubernetes implementations come with many different levels of components, from clusters down to individual containers.

“Some of the obstacles to monitoring Kubernetes environments stem from the very same traits that make it so attractive to organizations – for instance, their distributed, dynamic nature comprising many layers of resources,” said Ozan Unlu, CEO and Founder of Edge Delta. “This generates high volumes of data from many disparate sources, making it hard for developers to analyze 100 percent of their data. The new features we are announcing enable developers to fully leverage all of their data, helping to maximize the many benefits of Kubernetes implementations.”

Automatic Observability

Edge Delta builds an instant history of service behavior going back to the service’s first deployment and establishes baselines of this activity when it is deployed in a Kubernetes environment. Three new features are made available by this functionality to assist in overcoming the issues mentioned above:

  • Kubernetes Overview – Developers may access a visual map of all of their Kubernetes clusters and the resources contained in them at any given time through the Edge Delta user interface. This aids in their understanding of the services being watched, their basic makeup, and their high-level behavior. Developers can swiftly navigate through log patterns from this screen to immediately spot changes in activity for a deeper analysis or drill down into specific resources to assess their health.
  • Kubernetes Automated Findings – Edge Delta automatically detects aberrant activity within enormous quantities of data as it baselines the behavior of Kubernetes components and learns what is ‘normal.’ This feature will mark the affected components, activate contextual warnings, and report this information to preferred destinations if an anomaly or other unusual occurrence is discovered, such as the log patterns of a namespace or subset of containers deviating from the norm. This would facilitate faster problem identification and resolution for developers, even at the most minute levels, without the need to configure and iteratively improve complex logic.
  • Kubernetes Findings View – Through Findings View, the Automated Findings described above are displayed. This screen’s major objective is to make it easier to distribute insights to other team members; with just a few clicks, any discovery may be shared with the relevant person. Additionally, from this screen, behavior can be categorized by native elements like namespaces or containers, facilitating teamwork and accelerating problem-solving.

“These updates augment the value Edge Delta already delivers, by giving developers always up-to-the-second, automatic observability into their mission-critical Kubernetes resources,” added Mr. Unlu. “Because this happens within seconds, developers can detect anomalies more efficiently and stay one step ahead of system health issues, and avoid relying on DevOps or SRE team members.”