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Efficient SLO event integration powers successful AIOps

Dynatrace

For instance, consider how fine-tuned failure rate detection can provide insights for comprehensive understanding. Please refer to How to fine-tune failure detection (dynatrace.com) for further information. Let’s assume we created a service-availability SLO, monitoring the request failure count against the overall request counts.

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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

You will need to know which monitoring metrics for Redis to watch and a tool to monitor these critical server metrics to ensure its health. Redis returns a big list of database metrics when you run the info command on the Redis shell. You can pick a smart selection of relevant metrics from these.

Metrics 130
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Observability engineering: Getting Prometheus metrics right for Kubernetes with Dynatrace and Kepler

Dynatrace

This challenge has given rise to the discipline of observability engineering, which concentrates on the details of telemetry data to fine-tune observability use cases. To get a more granular look into telemetry data, many analysts rely on custom metrics using Prometheus.

Metrics 186
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9 key DevOps metrics for success

Dynatrace

The emerging concepts of working with DevOps metrics and DevOps KPIs have really come a long way. DevOps metrics to help you meet your DevOps goals. Like any IT or business project, you’ll need to track critical key metrics. Here are nine key DevOps metrics and DevOps KPIs that will help you be successful.

DevOps 209
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Automate complex metric-related use cases with the Metrics API version 2

Dynatrace

Dynatrace collects a huge number of metrics for each OneAgent-monitored host in your environment. Depending on the types of technologies you’re running on individual hosts, the average number of metrics is about 500 per computational node. Running metric queries on a subset of entities for live monitoring and system overviews.

Metrics 234
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Flexible, scalable, self-service Kubernetes native observability now in General Availability

Dynatrace

From a cost perspective, internal customers waste valuable time sending tickets to operations teams asking for metrics, logs, and traces to be enabled. A team looking for metrics, traces, and logs no longer needs to file a ticket to get their app monitored in their own environments. This approach is costly and error prone.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume. In Kafka Streams, a large configuration space is available for potential optimizations. Recovery time of the throughput metric. Recovery time of the latency p90.