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Sustainability: Thoughts from a software engineer

Dynatrace

Collect metrics on energy consumption or derive them from existing signals. For example, reporting jobs can process monthly data without running exactly at the end of the month. Platform engineers can set defaults for development teams, such as the number of replicas a service should have or whether it scales automatically.

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Part 2: A Survey of Analytics Engineering Work at Netflix

The Netflix TechBlog

This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Each format has a different production process and different patterns of cash spend, called our Content Forecast. Need to catch up?

Analytics 190
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Observability engineering: Getting Prometheus metrics right for Kubernetes with Dynatrace and Kepler

Dynatrace

For busy site reliability engineers, ensuring system reliability, scalability, and overall health is an imperative that’s getting harder to achieve in ever-expanding, cloud-native, container-based environments. To get a more granular look into telemetry data, many analysts rely on custom metrics using Prometheus. What is Prometheus?

Metrics 245
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Next generation Dynatrace Davis AI becomes the default causation engine

Dynatrace

Back during Perform 2019, we introduced the next generation of the Dynatrace AI causation engine , also known as Davis. becomes the default causation engine and will replace the previous version as the default for all new environments. as the default AI engine. AI causation engine. All existing Davis 1.0

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Transform data into insights with Dynatrace Dashboards and Notebooks

Dynatrace

You can now: Kickstart your creation journey using ready-made dashboards Accelerate your data exploration with seamless integration between apps Start from scratch with the new Explore interface Search for known metrics from anywhere Let’s look at each of these paths through an end-to-end use case focused on Kubernetes monitoring.

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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. It forms the cornerstone of chaos engineering experiments. Why reliability?

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Reliability indicators that matter to your business: SLOs for all data types

Dynatrace

This lets you build your SLOs around the indicators that matter to you and your customers—critical metrics related to availability, failure rates, request response times, or select logs and business events. Hence, having a dedicated dashboard tile visualizing the key parameters of each SLO simplifies the process of evaluating them.

Metrics 277