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Build and operate multicloud FaaS with enhanced, intelligent end-to-end observability

Dynatrace

For example, to handle traffic spikes and pay only for what they use. Scale automatically based on the demand and traffic patterns. Observability is typically achieved by collecting three types of data from a system, metrics, logs and traces. The elasticity of serverless services helps organizations scale as needed.

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Dynatrace simplifies StatsD, Telegraf, and Prometheus observability with Davis AI

Dynatrace

Open-source metric sources automatically map to our Smartscape model for AI analytics. We’ve just enhanced Dynatrace OneAgent with an open metric API. Here’s a quick overview of what you can achieve now that the Dynatrace Software Intelligence Platform has been extended to ingest third-party metrics. Dynatrace news.

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How digital experience monitoring helps deliver business observability

Dynatrace

Fast, consistent application delivery creates a positive user experience that can ultimately drive customer loyalty and improve business metrics like conversion rate and user retention. It is proactive monitoring that simulates traffic with established test variables, including location, browser, network, and device type.

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Best practices for alerting

Dynatrace

For instance, when there isn’t enough traffic (late at night), the AI will not act to avoid alert spamming. It doesn’t apply to infrastructure metrics such as CPU or memory. Unless you use our log analytics solution, Dynatrace doesn’t even look at log files to decide whether something is failing.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

We took a hybrid head-based sampling approach that allows for recording 100% of traces for a specific and configurable set of requests, while continuing to randomly sample traffic per the policy set at ingestion point.

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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. With Grail, we have reinvented analytics for converged observability and security data,” Greifeneder says.

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Kubernetes vs Docker: What’s the difference?

Dynatrace

This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. For a deeper look into how to gain end-to-end observability into Kubernetes environments, tune into the on-demand webinar Harness the Power of Kubernetes Observability. What is Docker? Networking.