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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing enables software engineers to model their applications’ business logic as high-level representations in a directed acyclic graph without explicitly defining a physical execution plan. We designed experimental scenarios inspired by chaos engineering. This significantly increases event latency.

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Enhancing Kubernetes cluster management key to platform engineering success

Dynatrace

Five of the most common include cluster instability, resource and cost management, security, observability, and stress on engineering teams. Engineering teams are overwhelmed with stuff to do.” ” First, Akamas collects metrics, then recommends configuration improvements and applies these recommendations. .

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AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

Micrometer is used for instrumenting both out-of-the-box and custom metrics from Spring Boot applications. Davis topology-aware anomaly detection and alerting for your Micrometer metrics. Topology-related custom metrics for seamless reports and alerts. Micrometer uses a registry to export metrics to monitoring systems.

Metrics 207
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AI-driven analysis of Spring Micrometer metrics in context, with topology at scale

Dynatrace

Micrometer is used for instrumenting both out-of-the-box and custom metrics from Spring Boot applications. Davis topology-aware anomaly detection and alerting for your Micrometer metrics. Topology-related custom metrics for seamless reports and alerts. Micrometer uses a registry to export metrics to monitoring systems.

Metrics 130
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AI-driven analysis of Spring Micrometer metrics in context, with topology at scale

Dynatrace

Micrometer is used for instrumenting both out-of-the-box and custom metrics from Spring Boot applications. Davis topology-aware anomaly detection and alerting for your Micrometer metrics. Topology-related custom metrics for seamless reports and alerts. Micrometer uses a registry to export metrics to monitoring systems.

Metrics 130
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How To Reduce MTTR

DZone

As a Site Reliability Engineer , one of the key metrics that I use to track the effectiveness of incident management is Mean Time To Recover (MTTR). A few examples of SLIs are error rate, latency, throughput, etc. 10 Things That Can Help Reduce the Mean Time to Recovery (MTTR) 1.

Latency 130
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How to Configure Istio, Prometheus and Grafana for Monitoring

DZone

One of the primary responsibilities of Site reliability engineers (SREs) in large organizations is to monitor the golden metrics of their applications, such as CPU utilization, memory utilization, latency, and throughput.