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DevOps monitoring tools: How to drive DevOps efficiency

Dynatrace

With the world’s increased reliance on digital services and the organizational pressure on IT teams to innovate faster, the need for DevOps monitoring tools has grown exponentially. But when and how does DevOps monitoring fit into the process? And how do DevOps monitoring tools help teams achieve DevOps efficiency?

DevOps 225
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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. Simply put, metrics are the counts and measures that are often calculated or aggregated over time.

Analytics 191
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What is predictive AI? How this data-driven technique gives foresight to IT teams

Dynatrace

But when these teams work in largely manual ways, they don’t have time for innovation and strategic projects that might deliver greater value. Predictive AI empowers site reliability engineers (SREs) and DevOps engineers to detect anomalies and irregular patterns in their systems long before they escalate into critical incidents.

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Weighing the top seven Kubernetes challenges and how to solve them

Dynatrace

Adopting this powerful tool can provide strategic technological benefits to organizations — specifically DevOps teams. This complexity has surfaced seven top Kubernetes challenges that strain engineering teams and ultimately slow the pace of innovation. Acceleration of innovation. What is Kubernetes?

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What is? OpenTelemetry??An open-source standard for logs, metrics, and traces

Dynatrace

Loosely defined, observability is the ability to understand what’s happening inside a system from the knowledge of the external data it produces, which are usually logs, metrics, and traces. Logs, metrics, and traces make up the bulk of all telemetry data. Monitoring begins here.

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Perform 2023 Guide: Organizations mine efficiencies with automation, causal AI

Dynatrace

They now use modern observability to monitor expanding cloud environments in order to operate more efficiently, innovate faster and more securely, and to deliver consistently better business results. DevOps metrics and digital experience data are critical to this. Learn more.

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Introducing Dynatrace built-in data observability on Davis AI and Grail

Dynatrace

million” – Gartner Data observability is a practice that helps organizations understand the full lifecycle of data, from ingestion to storage and usage, to ensure data health and reliability. The rise of data observability in DevOps Data forms the foundation of decision-making processes in companies across the globe.

DevOps 198