Rafia Aqil and a team of Microsoft experts deliver a comprehensive technical guide to designing, securing, and deploying Azure Databricks environments, spotlighting best practices for robust architecture and effective governance.

Architecting and Deploying Secure Azure Databricks Workspaces: Design, Network, and Access Best Practices

Authors: Chris Walk, Dan Johnson, Eduardo dos Santos, Ted Kim, Eric Kwashie, Chris Hayes, Tayo Akigbogun, Rafia Aqil Peer Reviewed: Mohamed Sharaf

Overview

This meticulously detailed guide walks through designing and deploying secure, scalable Azure Databricks environments, focusing on workspace architecture, network security, governance via Unity Catalog, and practical deployment steps using the Azure portal.

1. Design: Workspace, Subscription Organization, Analytics Architecture, and Compute

1.1 Workspace Organization

  • Align Databricks workspaces to business units (“Business Unit Subscription” pattern).
  • Streamline access control and enable cost transparency through clear tagging.
  • Limit workspace sprawl (recommendation: 20–50 workspaces per subscription).

1.2 Governance with Unity Catalog

  • Unity Catalog centralizes data governance (access control, lineage, auditing).
  • Shared metastore per Azure region allows uniform governance across workspaces.
  • Delta Sharing supports cross-region data access.

1.3 Subscription Segmentation

  • Segment environments (dev, test, prod) by separate Azure subscriptions for isolation, policy enforcement, and resource management.

1.4 Landing Zone Assessment

  • Conduct Microsoft Landing Zone reviews to assess best practices alignment before large deployments.

1.5 Reference Architectures

  • Use Microsoft’s solution references for: modern data platforms, ETL-intensive workloads, end-to-end analytics.

1.6 Compute Options in Databricks

  • Classic Compute: Fine-tuned cluster configuration (VM selection, autoscaling, SSD/HDD selection).
  • Serverless Compute: Managed clusters with instant scaling, best for agility and simplicity.
  • Billing: Based on Databricks Units (DBUs, per-second).

2. Network Planning: CIDR, VNet Injection, Security

2.1 CIDR Planning

  • Plan VNet/subnet sizes for cluster growth and concurrency requirements.
  • Recommendations: VNet CIDR /16–/24, subnets up to /26.

2.2 VNet Injection

  • Deploy customer-managed VNets for Databricks compute plane.
  • Supports secure integration, custom DNS, service endpoints, Azure firewall, and NSGs.
  • Private, public subnets, and additional private link subnets required for endpoints.

3. Secure Network Architecture

3.1 Secure Cluster Connectivity (No Public IP)

  • All Databricks clusters initiate outbound-only connections; no inbound public IPs.
  • Enforce via VNet Injection and NSGs.

3.2 Routing and Egress Control

  • Assign User Defined Routes (UDRs) to direct Databricks traffic to NVAs (Azure Firewall, NAT Gateway).
  • Use Azure service tags for control plane traffic.
  • Supports private endpoint access for web/REST/JDBC traffic.
  • Dedicated browser authentication endpoints for SSO (Microsoft Entra ID integration).
  • Manage DNS zones for seamless authentication.

3.4 Serverless Compute Networking

  • Serverless resources are isolated, managed by Databricks and connected over Microsoft backbone.
  • Use Network Connectivity Configurations (NCCs) for endpoint, firewall, and private access management.

4. Step-by-Step Deployment Using Azure Portal

4.1 Resource Group Setup

  • Create dedicated Resource Groups and deploy Databricks workspaces, VNets, subnets in the same region for performance/security.

4.2 Deploying VNet and Azure Firewall

  • Customize address ranges (e.g., 10.28.0.0/23, public/private/private-link subnets).
  • Enable Azure Firewall for egress control.

4.3 Databricks Workspace Creation

  • Enable Secure Cluster Connectivity and VNet Injection.
  • Configure NAT Gateway, disable public access, set NSG rules, define private endpoints, and DNS zones.
  • (Optional) Enable infrastructure encryption and compliance profiles (e.g., HIPAA).

4.4 Private Endpoints for SSO and Storage

  • Create dedicated workspaces for browser authentication endpoint per region.
  • Set up Azure Bastion for connectivity testing if no VPN/ExpressRoute.

4.5 Storage Account and Endpoint Configuration

  • Deploy ADLS Gen2 or Blob Storage with hierarchical namespace and disabled public access.
  • Set up private endpoints for dfs and blob sub-resources.

4.6 Linking Storage and Databricks

  • Create and assign Access Connectors (Managed Identity) with appropriate roles (Storage Blob Data Contributor).
  • Integrate credentials via Resource IDs and Unity Catalog External Location setup.

4.7 Serverless Networking (as required)

  • Create and link Network Connectivity Configurations (NCCs) and Private Endpoint Rules for serverless compute.
  • Approve pending private connections and validate status.

4.8 Workspace Validation

  • Test cluster launch, storage read/write (Spark configuration), DNS validation, and on-prem connectivity via ExpressRoute if needed.

4.9 Access Controls and Policies

  • Use Account Console to configure workspace roles, Unity Catalog, cluster policies.
  • Follow least privilege principles for user permissions and resource access.
  • Use tutorials and best practice articles for team onboarding.
  • Note DBFS security limitations and recommendations.

5. Data Migration Planning

  • Assess migration strategy (source systems, data volumes, ingestion method: batch/streaming/hybrid).
  • For complex migrations, leverage Microsoft Cloud Accelerated Factory support.

6. Ongoing Governance & Maintenance

  • Tag resources for cost and ownership tracking.
  • Continuously review configurations to adapt to new requirements/threats.
  • Monitor privilege assignments and enforce cluster access controls.

References and Further Reading

  • Microsoft documentation, best practices, and technical community blog posts on Databricks deployment, security, network architecture, and cloud integration.
  • Links provided throughout for solution architectures, cost optimization, advanced networking topics.

By following these hands-on and architectural steps, organizations can achieve well-governed, secure, and scalable Azure Databricks deployments ready for analytics and machine learning workloads.

This post appeared first on “Microsoft Tech Community”. Read the entire article here