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Dynatrace on Microsoft Azure in Australia enables regional customers to leverage AI-powered observability

Dynatrace

As modern multicloud environments become more distributed and complex, having real-time insights into applications and infrastructure while keeping data residency in local markets is crucial. Dynatrace on Microsoft Azure allows enterprises to streamline deployment, gain critical insights, and automate manual processes. The result?

Azure 278
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Microsoft Ignite 2024 guide: Cloud observability for AI transformation

Dynatrace

AI transformation, modernization, managing intelligent apps, safeguarding data, and accelerating productivity are all key themes at Microsoft Ignite 2024. Adopting AI to enhance efficiency and boost productivity is critical in a time of exploding data, cloud complexities, and disparate technologies.

Cloud 263
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Unifying SecOps and Observability for Enhanced Cloud Security in Azure

DZone

As someone who has worked in various areas of Azure for almost a decade, I have witnessed its constant evolution and the rise of increasingly sophisticated security risks. This calls for a unified approach to modern cloud security , where integrating Security Operations, aka "SecOps," into observability becomes essential.

Azure 147
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Dynatrace elevates data security with separated storage and unique encryption keys for each tenant

Dynatrace

Dynatrace continues to deliver on its commitment to keeping your data secure in the cloud. Enhancing data separation by partitioning each customer’s data on the storage level and encrypting it with a unique encryption key adds an additional layer of protection against unauthorized data access.

Storage 246
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Dynatrace AI-powered, unified observability is coming to Google Cloud

Dynatrace

Organizations are increasingly embracing cloud- and AI-native strategies, requiring a more automated and intelligent approach to their observability and development practices. Thats why Dynatrace will make its AI-powered, unified observability platform generally available on Google Cloud for all customers later this year.

Google 195
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Essential Guidelines for Building Optimized ETL Data Pipelines in the Cloud With Azure Data Factory

DZone

When building ETL data pipelines using Azure Data Factory (ADF) to process huge amounts of data from different sources, you may often run into performance and design-related challenges. This article will serve as a guide in building high-performance ETL pipelines that are both efficient and scalable.

Azure 306
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Log filtering made easy: Data segmentation and advanced filters in Dynatrace Logs

Dynatrace

Fast and efficient log analysis is critical in todays data-driven IT environments. Dynatrace segments simplify and streamline data organization in large and complex IT environments, providing pre-scoped data without compromising performance. Dynamic and flexible conditions Segments can leverage variables (e.g.,

AWS 217