Edge Delta announced a set of new features designed to simplify logging and overcome the challenges of monitoring Kubernetes environments.

Edge Delta's Kubernetes Overview, Kubernetes Automated Findings and Kubernetes Findings View are available immediately, and work natively out of the box without any extra configuration.

These features advance Edge Delta's approach to observability, which unlocks complete visibility and enables self-service observability for developers. Using Edge Delta, teams can:

- Analyze 100% of their raw data before it's indexed, so no data needs to be neglected;

- Understand the behavior of their services without manual operations; and

- Surface every issue – even those never seen before – along with the context around it.

"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," says Ozan Unlu, CEO and Founder, 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."

Kubernetes resources are constantly provisioned and deprovisioned - a pace that many development teams can't keep up with, making it nearly impossible to continually monitor their environments. Additionally, as a distributed architecture, Kubernetes environments create high data volumes and costs. Finally, Kubernetes implementations come with many different tiers of components - from clusters down to individual containers - and building logic at this level of granularity can be time-consuming, leaving developers unprepared and in a reactive position when a problem occurs.

When Edge Delta is deployed in a Kubernetes environment, it builds an instant history of service behavior dating back to the service's initial deployment and creates baselines of this activity. This functionality unlocks three new features to help overcome the challenges described above:

- Kubernetes Overview - From the Edge Delta user interface, developers gain a visual map detailing all their Kubernetes clusters and the resources within them at any given moment in time. This helps them understand what services are being monitored, what components they consist of, and what their behavior is at a high level. From this screen, developers can drill down into individual resources to gauge their health or quickly navigate into log patterns, allowing them to rapidly identify changes in behavior for a deeper investigation.

- Kubernetes Automated Findings - As Edge Delta baselines the behavior of Kubernetes components and understands what is 'normal,' it also automatically identifies abnormalities within huge volumes of data, like anomalous behavior. If an anomaly or otherwise interesting event is detected – such as the log patterns of a namespace or subset of containers deviating from the norm – this feature will flag the affected components, trigger contextual alerts and report this information to preferred destinations. This helps developers more quickly identify and resolve issues at even the most granular levels and without configuring and constantly refining complex logic.

- Kubernetes Findings View - The Automated Findings detailed above are surfaced through Findings View. The main purpose of this screen is to simplify the process of routing insights to various team members – in a matter of clicks, any finding can be shared with the appropriate party. Additionally from this screen, behavior can be organized by native components such as namespaces or containers, making team collaboration and issue resolution faster and easier.

"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," continues 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."