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Decreasing Carbon Emissions using Azure’s Big Data Compute

Posted by David Santana on Apr 1, 2021 11:45:00 AM

The accelerated growth of technology raises questions about its impact on climate; this is a concern we cannot ignore. A proper response to the climate crisis requires a deep-rooted commitment, one that promotes sustainable, re-usable, energy-efficient services, such as Microsoft Azure’s Big Data compute. This service drives innovation to decrease negative environmental effects. As an example, Microsoft’s modern global datacenters are an investment into an energy efficient future, leading the way in energy efficient data centers. Leading by example and demonstrating transparency into how they help organizations in every sector significantly reduce carbon impacts is one of the many benefits in migrating your big data workloads to the Microsoft Azure cloud ecosystem.

Gauging Sustainability


No matter what size your organization is you can measure the carbon impact of your Microsoft Azure Big Data workload, to help make informed, sustainable big data compute cluster provisioning decisions. You might be wondering how exactly you measure this carbon impact. Microsoft has a built-in tool, the Azure sustainable calculator, which estimates your workload’s carbon impacts in comparison to the on-premises datacenter’s carbon emissions.

Energy Efficient Big Data Compute Clusters


Current third-party studies from industry experts have compared Microsoft Azure Big Data compute with traditional datacenter compute and the results of those studies are promising.

The results show that on average Microsoft Compute is 70 percent more energy efficient than the monolithic on-premises data centers.

Furthermore, procuring Azure services includes renewable electricity which powers Microsoft Datacenters. If considered, carbon emissions from Big Data Azure compute clusters are on average 90 percent lower than the monolithic data centers.

Topics: Cloud Computing, Microsoft Azure, Big Data, ITTraining, Carbon Emissions, Sustainability, Compute Clusters

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