Deploying Multidimensional Models in Analysis Services

This guide provides detailed steps and considerations for deploying your SQL Server Analysis Services (SSAS) multidimensional models to a production environment. Proper deployment ensures that your analytical solutions are accessible, performant, and secure for end-users.

Prerequisites

  • A fully developed and tested SSAS multidimensional project in Visual Studio with SQL Server Data Tools.
  • Access to the target SSAS server with appropriate administrative permissions.
  • A deployment plan, including target server configuration and any necessary security settings.

Deployment Methods

There are several ways to deploy your SSAS multidimensional models:

1. Using Visual Studio (Development and Test Environments)

For development and testing, deploying directly from Visual Studio is often the most convenient method. This pushes the model to a development or test SSAS instance.

  1. Open your SSAS multidimensional project in Visual Studio.
  2. In the Solution Explorer, right-click on the project and select Deploy.
  3. The Deployment Wizard will guide you through selecting the target server, database name, and other deployment options.

2. Using SQL Server Management Studio (SSMS)

SSMS provides a robust interface for managing and deploying SSAS databases. This is commonly used for production deployments.

  1. Open SQL Server Management Studio and connect to your SSAS server.
  2. Right-click on the Databases folder and select Restore.
  3. Browse to your deployed SSAS project's XMLA file (typically generated during a Visual Studio build or by using the Deployment Wizard).
  4. Configure the restore options, including the target database name, and initiate the restore process.

3. Using XMLA Scripts

For automated deployments and more granular control, you can use XML for Analysis (XMLA) scripts. These scripts define the entire database structure and can be executed against the SSAS server.

You can generate XMLA scripts using the SSAS Deployment Wizard or by capturing the deployment operations in SSMS.


<?xml version="1.0" encoding="utf-8"?>
<CreateDatabase xmlns="http://schemas.microsoft.com/analysisservices/2003/engine" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <Name>YourNewAnalysisDatabase</Name>
  <Source>
    <File>C:\Deployments\YourModel.asdatabase</File>
  </Source>
</CreateDatabase>

4. Using Tabular Editor (Third-Party Tool)

Tabular Editor is a popular third-party tool that offers advanced capabilities for managing and deploying SSAS models, including both multidimensional and tabular. It supports scripting and automation.

Deployment Considerations

  • Target Server Configuration: Ensure the target SSAS instance is properly configured for performance, memory, and network access.
  • Security: Define and assign roles and permissions to control user access to the deployed database and its objects.
  • Data Sources: Verify that the data sources used by the model are accessible from the SSAS server and that connection strings are correctly configured for the production environment.
  • Processing: After deployment, the database will need to be processed to load the initial data. This can be done through SSMS, SQL Server Agent jobs, or AMO (Analysis Management Objects).
  • Backup and Restore: Implement a robust backup strategy for your deployed SSAS databases.
  • Environment Variables: Use variables within your SSAS project to manage differences between development, testing, and production environments (e.g., data source names, server names).
ℹ️ Note: Always test your deployment process thoroughly in a non-production environment before deploying to production.

Post-Deployment Tasks

  • Processing: Schedule and execute full or incremental processing of the deployed database.
  • Security Configuration: Assign users and groups to the appropriate roles defined in your SSAS project.
  • Querying: Connect to the deployed database using client tools like Power BI, Excel, or Reporting Services to verify data integrity and performance.

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