MSDN Documentation

Automating Deployments

This document outlines strategies and tools for automating the deployment of SQL Server Analysis Services (SSAS) models. Automating deployments reduces manual errors, ensures consistency, and accelerates release cycles.

Why Automate SSAS Deployments?

  • Reduced Errors: Manual deployments are prone to human error. Automation ensures steps are executed consistently.
  • Faster Releases: Deployments can be executed quickly with minimal human intervention.
  • Reproducibility: Automated scripts ensure that deployments are repeatable and traceable.
  • Integration with CI/CD: Facilitates integration into Continuous Integration and Continuous Delivery pipelines.

Key Technologies and Tools

1. Tabular Editor

Tabular Editor is a powerful, open-source tool for developing and managing Tabular models. It supports scripting with C# and can be used to automate many aspects of model development and deployment.

  • Scripting: Use C# scripts to perform tasks like refreshing data, partitioning, deploying to different environments, and generating deployment scripts.
  • Command-line Interface (CLI): Integrate Tabular Editor's CLI into build scripts for unattended deployments.

For more information, visit the Tabular Editor GitHub repository.

2. SQL Server Data Tools (SSDT) with MSBuild

SSDT allows you to develop SSAS models within Visual Studio. The project files can be built using MSBuild, which can be scripted to deploy models.

A typical MSBuild command for deploying an SSAS project might look like this:

msbuild YourProject.csproj /t:Deploy /p:TargetServer=YourServerName;TargetDatabase=YourDatabaseName

3. PowerShell Scripts

PowerShell is a versatile scripting language for Windows administration. You can use PowerShell cmdlets to interact with SSAS.

  • SQLAS-Cmdlets: A module that provides cmdlets for managing SSAS, including deployment operations.
  • AMO (Analysis Management Objects): Programmatically interact with SSAS using AMO libraries within PowerShell.

Example PowerShell Snippet (Conceptual):


Import-Module SqlServer
$server = New-Object Microsoft.AnalysisServices.Server
$server.Connect("YourServerName")

$db = $server.Databases.GetByName("YourDatabaseName")
$db.Deploy("YourModelPackage.asdatabase", "NewDatabaseName", $true)
$server.Disconnect()

4. Azure DevOps / GitHub Actions (CI/CD Pipelines)

For cloud-based deployments or modern development workflows, Azure DevOps Pipelines or GitHub Actions are excellent choices. They allow you to define your build and release processes as code.

  • Tasks: Utilize built-in or custom tasks for SSAS deployment. This often involves running MSBuild or PowerShell scripts within the pipeline.
  • Artifacts: Package your SSAS project as a deployable artifact.
  • Environments: Define different deployment environments (Dev, Test, Prod) with appropriate configurations.

Deployment Strategies

1. Incremental Deployment

Deploy only the changes made to the model, rather than rebuilding and redeploying the entire model. This is typically handled by tools like Tabular Editor or specific MSBuild targets.

2. Full Deployment

Replace the existing model with a new version. This is simpler but can be slower and might involve more downtime.

3. Parameterization

Use parameters in your deployment scripts or pipeline configurations to specify target server names, database names, or other configuration settings that vary between environments.

Note:

Always test your automated deployment scripts thoroughly in a development or test environment before applying them to production.

Best Practices for Automation

  • Version Control: Store all deployment scripts and pipeline definitions in a version control system (e.g., Git).
  • Configuration Management: Separate configuration settings from deployment scripts using parameters or configuration files.
  • Idempotency: Design scripts so that running them multiple times has the same effect as running them once.
  • Rollback Strategy: Have a plan in place for rolling back a deployment if something goes wrong.
  • Monitoring: Monitor your deployment processes for success or failure.

Tip:

Consider using a deployment framework like Octopus Deploy or integrating directly with Azure DevOps or GitHub Actions for robust release management capabilities.

Further Reading