Databricks runs best on Azure

Opting for Azure Databricks can enhance your entire data journey in a single, adaptable environment.

Why Choose Azure Databricks Over Other Cloud Providers?

This article builds upon the Azure Databricks: Differentiated Synergy post and further explores Azure Databricks’ unique advantages in cloud data analytics and AI.

Azure Databricks: Driving Analytics for Modern Enterprises

In our data-centric world, businesses are searching for analytics platforms that offer easy management, effortless scalability, and reliable performance. Although Databricks is available on several major cloud platforms, not every version is the same. Azure Databricks is a Microsoft-first application, created collaboratively with Databricks, known for its excellent integration, performance, and governance features. It provides robust performance for tasks such as decision support systems (DSS), and works seamlessly with the Microsoft suite—including tools like Azure AI Foundry, Microsoft Power BI, Microsoft Purview, Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Entra ID, Microsoft Fabric, and much more. Choosing Azure Databricks simplifies your entire data journey—from data engineering and ETL processes to machine learning, AI, and business intelligence—all within a single, scalable platform.

Performance That Counts

Principled Technologies (PT), an independent technology assessment firm, has recently evaluated the performance of Azure Databricks and its version on Amazon Web Services (AWS). According to PT, Azure Databricks, the Microsoft-backed service, was significantly faster, boasting speeds up to 21.1% quicker for single query streams and saving over nine minutes on four simultaneous query streams.

The faster execution for individual queries reflects a smoother experience for each user. For instance, data engineers, scientists, and analysts can reduce the time spent on comprehensive reports, allowing the system to manage demanding analytical requests without competing for resources.

Similarly, speedy concurrent query performance ensures multiple users enjoy a more efficient experience while conducting analyses simultaneously. This means your analysts from various departments can quickly generate reports or dashboards at the same time, effectively sharing cluster resources.

To AutoScale or Not?

If keeping costs low is your priority, we suggest enabling autoscale for your Azure Databricks cluster. When certain segments of your data pipeline demand more computational power, autoscale helps add these resources and removes them when the demand decreases. This approach can save you money compared to keeping your compute fixed. When assessing the total cost of ownership (TCO) for your data and AI platforms, remember to consider integration and optimisation along with the data gravity. Generally, an autoscaling cluster is one of the most economical choices, although it might be slower. If consistent performance is crucial for you, consider turning off autoscaling.

Key Distinctions: Azure Databricks vs. Other Cloud Versions

Though all three cloud service providers (CSPs) offer Databricks, several aspects set Azure Databricks apart:

• Infrastructure: Azure Databricks is finely tuned for Azure Data Lake Storage (ADLS), while AWS and Google Cloud utilize their respective storage solutions.
• Management Layer: The different control frameworks influence billing, access management, and resource allocation.
• Ecosystem Compatibility: Azure Databricks is natively linked with Microsoft tools such as Power BI, Microsoft Fabric, Microsoft Purview, Azure AI Foundry, Power Platform, Copilot Studio, Entra ID, and others.
• Cost Structures: Each CSP has its own pricing structure, so it’s wise to estimate your anticipated expenditures based on your requirements.

Azure-Native Features: Enhancing Data and AI

Azure Databricks offers a variety of Azure-native functionalities that enhance analytics, governance, and security:

• Centralised Billing and Support: Manage everything through Azure’s portal, benefiting from unified support from both Microsoft and Databricks.
• Identity & Access Management: Utilise Microsoft Entra ID for smooth authentication, with Azure’s role-based access control (RBAC) for precise permissions.
• DevOps Compatibility: Native support for Git (Azure Repos) and CI/CD (Azure Pipelines) facilitates ease of deployment and teamwork.
• Credential Passthrough: Ensures user-specific permissions while accessing ADLS.
• Azure Key Vault: Securely manage sensitive information right within Databricks notebooks.
• Machine Learning Integration: Advanced integration with Azure Machine Learning for tracking experiments, managing models, and one-click deployments from Databricks to Azure ML endpoints.
• Azure Confidential Computing: Protects data being used through hardware-based Trusted Execution Environments, preventing unauthorised access—even from cloud operators.
• Azure Monitor: After signing in with Microsoft Entra ID, users gain access to Azure Databricks, Azure Data Lake Storage, and Azure Monitor from one dashboard, creating a streamlined and secure analytics ecosystem.

Cross-Cloud Governance: Unified Management Across Different Providers

Azure Databricks now supports cross-cloud data governance, making it easy to access and manage AWS S3 data through Unity Catalog—without the hassle of migrating or duplicating data. This cohesive strategy allows for standardised policies, access controls, and auditing across Azure and AWS, streamlining operations and boosting security in hybrid and multicloud setups.

Smooth Integration with the Microsoft Ecosystem

Azure Databricks is the sole Databricks offering that integrates thoroughly with Microsoft’s ecosystem. Its latest features include:

• Mirrored Azure Databricks Catalog in Microsoft Fabric: This functionality allows access to Databricks Unity Catalog metadata and tables directly from Microsoft Fabric, creating a unified analytics experience while removing data movement or duplication, particularly for serving to Power BI via Direct Lake mode.
• Power Platform Connector: Quickly connect Power Apps, Power Automate, and Copilot Studio to Azure Databricks, ensuring governed access to enterprise data and empowering users to create intelligent, data-centric applications without complicated setups.
• Azure AI Foundry Data Connection: Native integration allowing organisations to utilise real-time data from Azure Databricks for crafting responsible and governed AI solutions.

What This Means for You

Azure Databricks promises outstanding performance, cost-effectiveness, and seamless integration with Microsoft’s reliable cloud services. With features like centralised management, enhanced security, cross-cloud governance, and speed advantages, businesses can scale their analytics and AI initiatives, obtain faster insights, and improve operational efficiency using Azure Databricks.

Begin your journey with Azure Databricks today and see why it’s the ideal platform for your data and AI projects.

For further insights into Azure Databricks’ performance, dive into the complete Principled Technologies report.

Discover how Azure Databricks operates and access more information about the service at Databricks.com.

Learn More About Why Databricks Performs Best on Azure:

¹Azure, “Best practices for cost optimisation,” June 6, 2025, https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/cost-optimization/best-practices.

²Azure, “Best practices for performance efficiency,” June 6, 2025, https://learn.microsoft.com/en-us/azure/databricks/lakehouse-architecture/performance-efficiency/best-practices.