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Google Cloud Next 2024: AI innovation for Google Cloud

Dynatrace

In today’s rapidly evolving landscape, incorporating AI innovation into business strategies is vital, enabling organizations to optimize operations, enhance decision-making processes, and stay competitive. The annual Google Cloud Next conference explores the latest innovations for cloud technology and Google Cloud.

Google 258
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Ingesting JMeter, temperature and humidity metrics: A Dynatrace innovation day report

Dynatrace

Dynatrace has recently enhanced its Metrics APIs, allowing everyone to send any type of metric with any set of data dimension to Davis, Dynatrace’s AI engine. In our conversation, I mentioned the new Dynatrace Metrics ingestion and off we went. ?? There are many use cases for using this API.

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What is observability? Not just logs, metrics and traces

Dynatrace

In IT and cloud computing, observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces. An advanced observability solution can also be used to automate more processes, increasing efficiency and innovation among Ops and Apps teams.

Metrics 363
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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. In many cases, metric behavior changes over time.

Metrics 207
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How Park ‘N Fly innovates with IT automation, AIOps, and observability

Dynatrace

AIOps and observability for infrastructure management. This kind of IT automation “ingests data from every layer in the stack — from the infrastructure layer to the application layer and even user experience data,” says Bipin Singh, director of product marketing at Dynatrace. Previously, Park ‘N Fly had to manually troubleshoot.

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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. The goal is to turn more data into insights so the whole organization can make data-driven decisions and automate processes. Grail can store and process 1,000 petabytes per day,” Greifeneder explains.

Analytics 182
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The value of open platforms: How an open software intelligence platform accelerates innovation

Dynatrace

Software should forward innovation and drive better business outcomes. Conversely, an open platform can promote interoperability and innovation. Legacy technologies involve dependencies, customization, and governance that hamper innovation and create inertia. Data supports this need for organizations to flex and modernize.