Azure Analysis Services

Manage Models in Azure Analysis Services

This document provides a comprehensive guide to managing models within Azure Analysis Services. Learn how to create, deploy, connect to, and secure your tabular and multidimensional models.

Introduction to Model Management

Azure Analysis Services (AAS) is a fully managed Platform as a Service (PaaS) that provides enterprise-grade data modeling capabilities. Managing your models effectively is crucial for performance, security, and usability. This section covers the fundamental concepts and best practices for model management.

Creating and Developing Models

You can create models using various tools, primarily:

• SQL Server Data Tools (SSDT): A powerful Visual Studio extension for developing tabular and multidimensional models.
• Azure Portal: Provides a simplified interface for managing existing models and performing basic operations.

When developing models, consider the following:

• Data Source Connectivity: Ensure secure and efficient connections to your data sources.
• Data Modeling Best Practices: Design your tables, relationships, and calculations for optimal query performance and user experience.
• Partitioning: Implement partitioning strategies for large datasets to improve query performance and manageability.

Deploying Models

Once your model is developed, you can deploy it to your Azure Analysis Services server. This process typically involves:

1. Building the model project in SSDT.
2. Deploying the generated model files (e.g., .bim for tabular) to your AAS instance.

Deployment can be automated using CI/CD pipelines for greater efficiency and reliability.

# Example deployment command using Azure CLI
az aas models deploy --server <your-server-name> --resource-group <your-resource-group> --project <path-to-model-project>

Connecting to Models

Business intelligence tools and applications connect to Azure Analysis Services models to query data. Common connection methods include:

• Power BI: Connect directly to AAS models for interactive dashboards and reports.
• Excel: Use Power Pivot or the legacy Get & Transform Data features.
• SQL Server Management Studio (SSMS): For administrative tasks and querying.
• DAX Studio and Tabular Editor: Powerful tools for advanced analysis and model manipulation.

The connection string typically includes the server name, database name, and authentication method.

Managing Permissions

Controlling access to your models and data is critical. Azure Analysis Services supports role-based access control:

• Server Administrator Role: Full control over the AAS server.
• Database Roles: Define roles within each model database with specific read or read/write permissions.

Permissions can be managed through the Azure portal or programmatically using TOM (Tabular Object Model) or AMO (Analysis Management Objects).

Backups and Restores

Regular backups are essential for disaster recovery and data protection. Azure Analysis Services offers automatic backup functionality.

• Automated Backups: Configure daily or more frequent backups stored in Azure Blob Storage.
• Manual Backups: Initiate on-demand backups as needed.

Restoring a database from a backup can be done via the Azure portal, PowerShell, or REST API.

For more advanced scenarios, refer to the specific documentation for Azure Analysis Services development and administration.