- 08 Nov, 2025
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Databricks runs best on Azure
Why Databricks Runs Best on Azure: A Synergy for Unmatched Data and AI Performance
In the world of big data and AI, choosing the right platform is more than just a technical decision—it’s a strategic one. You have a universe of powerful tools at your disposal, but when two industry leaders join forces, the result is greater than the sum of its parts. This is precisely the case with Databricks, the Data and AI company, and Microsoft Azure.
While Databricks is available on multiple clouds, there’s a compelling argument that it runs best on Azure. It’s not just about running; it’s about thriving. The native integration, joint engineering, and a shared vision for an open data ecosystem create an environment where data teams can innovate faster, scale effortlessly, and drive tangible business value.
Let’s dive into the key reasons why Azure is the premier cloud for the Databricks Lakehouse Platform.
1. Native Integration and First-Party Service Experience
This is the single biggest advantage. Unlike a simple “bring-your-own-license” model, Databricks on Azure is a first-party service, jointly engineered by Microsoft and Databricks. This means:
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Unified Management: You can provision, manage, and monitor your Databricks workspaces directly from the Azure Portal. It appears right alongside your other Azure services like Synapse Analytics and Virtual Machines.
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Seamless Billing: Everything is consolidated into a single, monthly Azure bill. This simplifies finance operations (FinOps) and provides clear cost visibility without managing separate vendor invoices.
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Azure Native Security: Databricks automatically integrates with Azure Active Directory (Azure AD). This enables single sign-on (SSO) and allows you to apply the same robust security and compliance policies you use for the rest of your Azure estate directly to your Databricks workspace.
2. The Power of Delta Lake and Azure Data Lake Storage (ADLS) Gen2
At the heart of the Databricks Lakehouse is Delta Lake, an open-format data storage layer that brings reliability and performance to your data lakes. On Azure, this is supercharged by its deep integration with Azure Data Lake Storage (ADLS) Gen2.
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Direct Data Access: Databricks can read from and write to ADLS Gen2 with the highest possible throughput, using the Azure Blob File System (ABFS) driver. This eliminates the inefficiencies of gateways or connectors.
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Unified Data Governance: Your data resides in one place—ADLS Gen2. This is crucial for applying a consistent data governance strategy using Azure Purview, preventing the creation of siloed data copies.
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Cost-Effective Tiering: Leverage ADLS Gen2’s hot, cool, and archive storage tiers to automatically optimize storage costs for your Delta Lake tables without any complex manual processes.
3. Unified Data Governance with Azure Purview
In a modern data ecosystem, knowing where your data is, who is using it, and its lineage is non-negotiable. The integration between Databricks and Azure Purview creates a unified governance plane.
You can automatically scan your Databricks workspace to discover and classify sensitive data. Track the full lineage of data assets as they move from Azure Data Factory pipelines, through Databricks transformations, and into Power BI reports. This end-to-end visibility is critical for compliance, security, and building trust in your data.
4. Unmatched Synergy with the Azure AI/ML Ecosystem
Databricks isn’t just for data engineering; it’s a powerhouse for machine learning. On Azure, it connects seamlessly with the broader AI stack:
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MLflow on Azure: The open-source MLflow platform, built by Databricks, integrates natively with Azure Machine Learning. This allows you to track experiments, package models, and deploy them to Azure ML’s managed endpoints, creating a seamless MLOps workflow.
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Power BI Direct Connector: Analyze the data in your Databricks Lakehouse in real-time with Power BI. Business analysts can connect directly to Databricks SQL Warehouses, enabling self-service analytics on the most current data without complex ETL processes.
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Azure Synapse Analytics: For large-scale data warehousing scenarios, you can use Azure Synapse Analytics alongside Databricks in a modern medallion architecture, reading directly from the same Delta Lake tables in ADLS Gen2.
5. Enterprise-Grade Security and Compliance
Microsoft Azure invests billions in cybersecurity, and this protection extends to your Databricks environments.
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Managed VNet Injection: You can deploy your Databricks workspace directly into your own Azure Virtual Network (VNet), giving you fine-grained control over network security, firewalls, and data exfiltration prevention.
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Customer-Managed Keys (CMK): Use Azure Key Vault to hold your encryption keys, ensuring you have full control over your data at rest.
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Compliance: Benefit from Azure’s extensive portfolio of compliance certifications (like SOC, ISO, HIPAA, GDPR), which are inherited by your Databricks workspace.
6. A Shared Commitment to Openness
Both Microsoft and Databricks are deeply committed to open-source and open standards. From supporting Apache Spark™ and Delta Lake to fostering a vibrant developer community, this shared philosophy ensures you are never locked into a proprietary format. Your data remains yours, in an open format, future-proofing your investments.
Conclusion: More Than Just a Platform, It’s a Partnership
Choosing to run Databricks on Azure isn’t just selecting a cloud provider; it’s leveraging a deep, strategic partnership. The native integrations, from identity management with Azure AD to unified governance with Purview, create a cohesive and streamlined experience that simply isn’t available elsewhere.
This synergy translates directly to business outcomes: faster time-to-insight, reduced operational overhead, lower total cost of ownership (TCO), and a robust foundation for scalable, enterprise-grade AI.
When your goal is to unify your data, analytics, and AI on a single, open platform, the most powerful and integrated path forward is clear: Databricks on Azure.
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