{"id":42512,"date":"2025-07-15T08:00:00","date_gmt":"2025-07-15T15:00:00","guid":{"rendered":""},"modified":"2025-07-18T08:05:10","modified_gmt":"2025-07-18T15:05:10","slug":"databricks-runs-best-on-azure","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/databricks-runs-best-on-azure\/","title":{"rendered":"Databricks runs best on Azure"},"content":{"rendered":"\n

Azure Databricks has clear advantages over other cloud service providers<\/h2>\n\n\n\n

This blog is a supplement to the <\/em>Azure Databricks: Differentiated Synergy<\/em><\/a> blog post and continues to define the differentiation for Azure Databricks in the cloud data analytics and AI landscape.<\/em><\/p>\n\n\n\n

Azure Databricks: Powering analytics for the data-driven enterprise<\/h2>\n\n\n\n

In today\u2019s data-driven world, organizations are seeking analytics platforms that simplify management, offer seamless scalability, and deliver consistent performance. While Databricks is available across major cloud service providers (CSPs), not all implementations are equal. Azure Databricks<\/a> is a first party Microsoft offering co-engineered by Microsoft and Databricks, which stands out for its superior integration, performance, and governance capabilities. It not only delivers strong performance for workloads like decision support systems (DSSs), but it also seamlessly integrates with the Microsoft ecosystem, including solutions such as Azure AI Foundry<\/a>, Microsoft Power BI<\/a>, Microsoft Purview<\/a>, Microsoft Power Platform<\/a>, Microsoft Copilot Studio<\/a>, Microsoft Entra ID<\/a>, Microsoft Fabric<\/a>, and much more. Choosing Azure Databricks can streamline your entire data lifecycle\u2014from data engineering and Extract Transform Load (ETL) workloads to machine learning (ML), AI, and business intelligence (BI)\u2014within a single, scalable environment.<\/p>\n\n\n\n

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Maximize the value of your data assets for all analytics and AI use cases<\/a><\/div>\n<\/div>\n\n\n\n

Performance that matters<\/h2>\n\n\n\n

Principled Technologies (PT), a third-party technology assessment firm, recently analyzed the performance of Azure Databricks and Databricks on Amazon Web Services (AWS). PT stated that Azure Databricks, the Microsoft first-party Databricks service, outperformed Databricks on AWS\u2014it was up to 21.1% faster for single query streams and saved over 9 minutes on four concurrent query streams.<\/p>\n\n\n

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Faster execution for a single query stream demonstrates the better experience a lone user would have. For example, data engineers, scientists, and analysts, and other key users could save time when running multiple detailed reports, tasking the system to handle heavy analytical queries without resource competition.<\/p>\n\n\n\n

Faster concurrent query performance demonstrates the better experience multiple users would have while running analyses at the same time. For example, your analysts from different departments can save time when running reports or dashboards simultaneously, sharing cluster resources.<\/p>\n\n\n\n

With or without autoscale?1, 2<\/sup><\/h2>\n\n\n\n

If cost is a top priority, we recommend autoscaling your Azure Databricks cluster<\/a>. When certain parts of your data pipeline are more computationally intensive, autoscale enables Azure Databricks to add compute resources and then remove them when the intensity cools down. This can help reduce your costs compared to static compute sizing. Considering the total cost of ownership (TCO) for data and AI platforms is essential, in addition to their integration and optimization capabilities combined with data gravity. An autoscaling cluster is often the most cost-effective option, though it may not be the fastest. If consistent performance is a top priority, consider disabling autoScale.<\/p>\n\n\n\n

Key differences: Azure Databricks versus Databricks on other clouds deployed as third party<\/h2>\n\n\n\n

While all three CSPs offer Databricks, several factors distinguish Azure Databricks:<\/p>\n\n\n\n