Azure Databricks vs Databricks: What are the differences?

Azure Databricks and Databricks are both powerful platforms for data engineering and analytics, but there are several key differences between the two.

1. Integration with Azure Services: Azure Databricks is deeply integrated with the Azure ecosystem, allowing seamless integration with other Azure services such as Azure Blob Storage, Azure Data Lake Storage, and Azure Machine Learning. This integration simplifies workflows and enables easy access to data and resources within the Azure environment. On the other hand, Databricks is a standalone platform that can be used with other cloud providers or on-premises infrastructure.

2. Managed Service: Azure Databricks is a managed service provided by Microsoft Azure, which means that Microsoft handles all the infrastructure management, including provisioning, upgrading, and scaling. This allows users to focus on their data and analytics tasks without worrying about system administration. Databricks, on the other hand, can be deployed as a managed service on multiple cloud providers or as an on-premises solution, requiring more user involvement in infrastructure management.

3. Enterprise-Grade Security: Azure Databricks offers enhanced security features such as platform encryption, network isolation, and integration with Azure Active Directory for user authentication and access control. It also supports Azure Virtual Network Service Endpoints, which allows secure access to Azure Databricks from virtual networks. While Databricks also provides robust security features, Azure Databricks leverages Azure's security capabilities to provide additional layers of enterprise-grade security.

4. Cost: The pricing model for Azure Databricks differs from Databricks. Azure Databricks pricing is based on usage, allowing users to choose the most cost-effective instance types and scaling options based on their workload requirements. It also offers integration with Azure Cost Management for monitoring and optimizing costs. Databricks pricing, on the other hand, is based on a subscription model, with different pricing tiers based on the features and support levels required.

5. Collaboration and Integration: Azure Databricks provides seamless collaboration through integration with Azure DevOps, Git repositories, and Azure Machine Learning. It allows teams to work together on data engineering and analytics projects, enabling version control, code reviews, and CI/CD pipelines. Databricks also supports collaboration features, but the integration capabilities of Azure Databricks make it easier to integrate into existing DevOps workflows and leverage other Azure services.

6. Azure Native Monitoring and Management: Azure Databricks leverages Azure Monitor for monitoring usage, performance, and job metrics. It also integrates with Azure Log Analytics and Azure Application Insights for in-depth monitoring and troubleshooting. Databricks offers its own monitoring and management tools, but the integration with Azure services in Azure Databricks provides a more unified and native monitoring experience for users within the Azure ecosystem.

In summary, Azure Databricks provides deep integration with the Azure ecosystem, a managed service approach with simplified infrastructure management, enhanced security features, flexible pricing options, better collaboration and integration capabilities, and native monitoring and management using Azure services. Databricks, on the other hand, offers a standalone platform that can be used with multiple cloud providers or on-premises infrastructure.