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The road to observability demo part 3: Collect, instrument, and analyze telemetry data automatically with Dynatrace

Dynatrace

Making applications observable—relying on metrics, logs, and traces to understand what software is doing and how it’s performing—has become increasingly important as workloads are shifting to multicloud environments. We also introduced our demo app and explained how to define the metrics and traces it uses.

Metrics 243
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Build systems more reliably with Dynatrace: Chaos Engineering

Dynatrace

This approach enhances key DORA metrics and enables early detection of failures in the release process, allowing SREs more time for innovation. This blog post explores the Reliability metric , which measures modern operational practices. Why reliability?

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Unlock the power of contextual log analytics

Dynatrace

Davis AI contextually aligns all relevant data points—such as logs, traces, and metrics—enabling teams to act quickly and accurately while still providing power users with the flexibility and depth they desire and need. Learn how Dynatrace can address your specific needs with a custom live demo.

Analytics 304
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How Dynatrace boosts production resilience with Site Reliability Guardian

Dynatrace

Ensure expected production behavior One Dynatrace team is responsible for the demo applications we use to demonstrate Dynatrace capabilities. We use monitored demo applications to deliver constant load and a defined set of business transactions. The queries are depicted below (sensitive data has been removed).

DevOps 246
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Observability vs. monitoring: What’s the difference?

Dynatrace

Monitoring focuses on watching specific metrics. Observability is the ability to understand a system’s internal state by analyzing the data it generates, such as logs, metrics, and traces. For example, we can actively watch a single metric for changes that indicate a problem — this is monitoring.

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What is full stack observability?

Dynatrace

A full-stack observability solution uses telemetry data such as logs, metrics, and traces to give IT teams insight into application, infrastructure, and UX performance. Observability can identify the baseline user experience and allow teams to improve it by optimizing page load times or reducing latency. See observability in action!

DevOps 303
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Extending Vector with eBPF to inspect host and container performance

The Netflix TechBlog

Today we are excited to announce latency heatmaps and improved container support for our on-host monitoring solution?—?Vector?—?to Remotely view real-time process scheduler latency and tcp throughput with Vector and eBPF What is Vector? to the broader community. Vector is open source and in use by multiple companies.