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Edge Delta Launches Support for Ingesting Kubernetes Metrics

Edge Delta is launching new support for Kubernetes metrics.

By automatically ingesting the most useful Kubernetes metrics, Edge Delta will help metrics-first teams improve the signal-to-noise ratio and reduce costs. Moreover, the release is tightly integrated with Edge Delta's logging capabilities, correlating metrics-based alerts directly to the relevant log data. This seamless experience helps customers quickly move from monitoring to troubleshooting and accelerate post-detection resolution times.

"This release helps observability practices control both the cost and noise of monitoring Kuberenetes-based environments," says Ozan Unlu, CEO of Edge Delta. "Teams shouldn't have to ingest and pay for metrics that go ignored. Moreover, troubleshooting issues shouldn't require you to look for a 'needle in the haystack.' With today's announcement, Edge Delta is helping teams realize more value and efficiency from observability."

With this release, Edge Delta leverages its architecture to distill metrics libraries into the ones that organizations access most frequently or might want an alert on, drastically reducing the number of metrics ingested.

Edge Delta also combines views of logs and metrics for faster resolution. Teams can easily drill into the relevant log data to quickly identify the root cause of issues.

Edge Delta aims to help teams get up and running quickly and build a launchpad for complete out-of-the-box observability. "Within five minutes of deployment, teams will be able to see all their logs and metrics," explains Unlu. When you use Edge Delta, metrics are then presented in the Edge Delta interface via out-of-the-box dashboards to accelerate time to value. With direct access to all of the metrics ingested, Edge Delta also gives the flexibility to build custom and unique views via embedded dashboards.

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Edge Delta Launches Support for Ingesting Kubernetes Metrics

Edge Delta is launching new support for Kubernetes metrics.

By automatically ingesting the most useful Kubernetes metrics, Edge Delta will help metrics-first teams improve the signal-to-noise ratio and reduce costs. Moreover, the release is tightly integrated with Edge Delta's logging capabilities, correlating metrics-based alerts directly to the relevant log data. This seamless experience helps customers quickly move from monitoring to troubleshooting and accelerate post-detection resolution times.

"This release helps observability practices control both the cost and noise of monitoring Kuberenetes-based environments," says Ozan Unlu, CEO of Edge Delta. "Teams shouldn't have to ingest and pay for metrics that go ignored. Moreover, troubleshooting issues shouldn't require you to look for a 'needle in the haystack.' With today's announcement, Edge Delta is helping teams realize more value and efficiency from observability."

With this release, Edge Delta leverages its architecture to distill metrics libraries into the ones that organizations access most frequently or might want an alert on, drastically reducing the number of metrics ingested.

Edge Delta also combines views of logs and metrics for faster resolution. Teams can easily drill into the relevant log data to quickly identify the root cause of issues.

Edge Delta aims to help teams get up and running quickly and build a launchpad for complete out-of-the-box observability. "Within five minutes of deployment, teams will be able to see all their logs and metrics," explains Unlu. When you use Edge Delta, metrics are then presented in the Edge Delta interface via out-of-the-box dashboards to accelerate time to value. With direct access to all of the metrics ingested, Edge Delta also gives the flexibility to build custom and unique views via embedded dashboards.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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