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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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Support of OpenTelemetry metrics with enhanced AI capabilities

Dynatrace

With the most important components becoming release candidates , Dynatrace now supports the full OpenTelemetry specification on all runtimes and automatically adds intelligence to metrics at enterprise scale. So these metrics are immensely valuable to SRE and DevOps teams. Automation and intelligence for metrics at enterprise scale.

Metrics 235
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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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Simplify observability for all your custom metrics (Part 2: OneAgent metric API)

Dynatrace

Welcome back to the blog series where we provide you with deep dives into the latest observability awesomeness from Dynatrace , demonstrating how we bring scale, zero configuration, automatic AI driven alerting, and root cause analysis to all your custom metrics, including open source observability frameworks like StatsD, Telegraf, and Prometheus.

Metrics 174
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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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Why applying chaos engineering to data-intensive applications matters

Dynatrace

The following are key insights from our extensive experimental analysis: Flink, Kafka Streams, and Spark Structured Streaming are resilient to different types and degrees of failure. From the Kafka Streams community, one of the configurations mostly tuned in production is adding standby replicas. Recovery time of the latency p90.

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Dynatrace innovates again with the release of topology-driven auto-adaptive metric baselines

Dynatrace

With the advent and ingestion of thousands of custom metrics into Dynatrace, we’ve once again pushed the boundaries of automatic, AI-based root cause analysis with the introduction of auto-adaptive baselines as a foundational concept for Dynatrace topology-driven timeseries measurements. Custom log metrics.

Metrics 220