Azure Data Explorer is now using AMD EPYC processor-based virtual machines

AMD has announced Azure Data Explorer, a Platform as a Service (PaaS) solution optimized for data exploration and near-real-time analytics, will now offer customers access to Microsoft Azure Virtual Machines featuring AMD EPYC processors.

In a collaboration between AMD, Azure compute and Azure Data Explorer, the Azure Data Explorer service is now offering the AMD EPYC processor-based Azure Dav4, Eav4, Easv4, and Lsv2 VMs for use.

The family of AMD EPYC processor-based Azure VMs enables Azure Data Explorer customers to gain up to 30-50% more performance on data analytics workloads for the same cost. With this performance and efficiency, Azure Data Explorer customers can improve the real-time analysis of large volumes of data streaming from applications, websites, IoT devices, and more.

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“The uplift in performance and efficiency capabilities provided by the AMD EPYC powered VMs on Microsoft Azure Data Explorer is another great proof point of the performance capabilities of our processors,” said Dan McNamara, senior vice president and general manager, Server Business Unit, AMD. “The AMD, Azure and Azure Data Explorer teams worked diligently together to provide this performance uplift for Azure Data Explorer, giving customers more capabilities to identify patterns, anomalies, and trends in their data to make better business decisions.”

“We’re pleased to offer Microsoft Azure VMs that feature AMD EPYC processors to Azure Data Explorer customers,” said Uri Barash, manager, Principle Group Program at Microsoft Corp. “As one of the largest workloads running on Azure and the analytical store for Microsoft online services including Office, Windows, and other internal properties, Azure Data Explorer is one of the most demanding services available and the AMD EPYC VMs have stood up to that challenge. Their performance combined with the software innovations with the latest version of Azure Data Explorer gives our customers even more performance and capabilities while they are running real-time analytics on huge volumes of data.”