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Databricks, in its cloud-agnostic version, can be deployed on AWS, Azure, Google Cloud or on-premise. Azure Databricks is the first-party version offered via the Azure portal, natively integrated with Microsoft services. Although they are based on the same Spark technology, their environments differ in terms of integration, management and security. This choice has an impact on cloud strategies and data governance.
Azure Databricks benefits from immediate integration with the Azure ecosystem: storage (Blob Storage and Data Lake Storage), Azure Machine Learning, Key Vault, Power BI and Synapse Analytics. Users can configure clusters and manage rights via the Azure interface, without having to manipulate keys or networks. The cloud-agnostic version of Databricks can run on multiple cloud providers or on-premises, bringing portability but requiring more configuration to connect storage, messaging and authentication services.
Azure Databricks is based on Microsoft’s managed service model: the underlying infrastructure (cluster creation, updates, security fixes) is taken care of, reducing operational overhead for data teams. This allows companies to concentrate on analytical tasks rather than cluster administration. Conversely, deploying Databricks autonomously requires management of the chosen cloud infrastructure (EC2 on AWS, Compute Engine on GCP…), network security and scalability. This freedom of customization can be an asset for organizations with specific needs, but increases the administration burden.
Azure Databricks fully integrates with Azure Active Directory for authentication, and uses Azure Key Vault to store and manage secrets. Unity Catalog is supported for data governance, and the service benefits from Azure compliance frameworks (ISO 27001, SOC 2, etc.). In the standard Databricks implementation, security depends on the underlying cloud provider and must be configured manually, including authentication (via OAuth or tokens), secret management and network policies. The advantage is greater flexibility on hybrid environments, but this requires cloud security skills and increased monitoring.
On Azure, Databricks logs and metrics are centralized via Azure Monitor, Log Analytics and Application Insights, offering unified visibility on performance and compliance. On the other hand, independent Databricks offers native monitoring tools (including Spark UI and Job Manager), but their integration with third-party solutions requires additional parameterization. Multi-cloud enterprises therefore need to plan for appropriate monitoring solutions.
Azure Databricks offers seamless integration with Power BI, Synapse and OneLake for visualization and analytics. Users can create dashboards, run SQL queries and explore data without leaving the Azure environment. In the cloud-agnostic version, it is possible to connect BI tools such as Tableau, Looker or Superset, but this requires more configuration. With this flexibility comes greater responsibility for managing access and latency.
Azure Databricks charges for DBUs based on cluster size and time of use, in addition to Azure compute costs. The system integrates with Azure Cost Management and offers pre-purchase discounts. Databricks independent also adopts a per-use model, but rates vary according to the provider and architecture chosen.
When should you choose Azure Databricks? When the organization is mostly on Azure, wants to reduce administrative tasks and benefit from native integrations (Power BI, Synapse, Key Vault). It’s also relevant in environments subject to strict standards, thanks to compliance certifications.
When should you choose Databricks in multi-cloud mode? When the strategy is to deploy workloads across multiple cloud providers, or to avoid vendor lock-in. This choice is relevant for companies with specific deployment requirements, or who want to take advantage of the best services from each cloud.
Are Azure Databricks and Databricks technically identical? They share the same technology base (Spark, Delta Lake), but Azure Databricks offers fully native integration and management in Azure, while standalone Databricks is multi-cloud and requires more configuration.
What are the security advantages of Azure Databricks? Authentication via Azure AD, integration with Key Vault and enforcement of Azure security policies simplify governance and compliance.
Why choose a multi-cloud deployment? To benefit from portability, avoid being locked in with a provider and run specific services on AWS or GCP, despite more complex management.
Which solution is easiest to supervise? Azure Databricks centralizes logs and metrics in Azure Monitor and Log Analytics. The standalone version requires configuration of integration with third-party monitoring solutions.