How to Migrate from AAS to Power BI: Step-by-Step Guide

Why Move from AAS to Power BI?

Migrating from Azure Analysis Services (AAS) to Microsoft Power BI Premium consolidated BI capabilities into a single platform. 

• Unified platform: Power BI provides a single platform to manage data models, reports, and dashboards in one workspace. Moving from AAS eliminates the need to manage separate semantic models and reporting layers. Thereby, it reduces the infrastructure complexity and ongoing maintenance charges.
• Enhanced Performance and Scalability: With features such as incremental refresh, composite models, and AI-powered insights, Power BI enhances performance and allows large, complex datasets to be processed more efficiently. Power BI gives better handling of load, concurrency, and flexible scaling.
• Cost Optimization: AAS operates on a capacity-based pricing model. It increases the cost indirectly. Power BI offers more flexible licensing options, offering access to premium features for developers. 
• Enhanced Self-service and Collaboration: The streamlined self-service analytics and easier report creation in Power BI give us a better user experience. Features such as shared datasets, integration, and workspaces improve collaboration.

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Challenges faced during the AAS to Power BI Migration and ways to overcome

Several challenges mentioned below related to compatibility, permissions, and feature gaps can be addressed through preparation and targeted workarounds.

• Model compatibility limitations: Main challenges to be addressed in AAS to Power BI migration are differences in feature support and behaviour between AAS and Power BI datasets. To overcome this, use the Tabular editor for pre-migration compatibility upgrades and scripting refreshes via XMLA endpoints. Analyze and audit the supported features using Poser BI-native capabilities such as composite models and aggregations.
• Performance and feature gaps: Post-migration refreshes fail due to credential resets or a lack of incremental processing. Memory usage between AAS and PBI Premium, refresh patterns, and potential cost overruns if not optimized. To overcome this, perform a detailed CPU analysis and optimize refresh schedules. 
• DAX calculations: Due to model redesign, some calculations might differ in Power BI. To overcome this, validate all critical measures by comparing both AAS and Power BI in parallel. Ensure that all calculations are validated by the business stakeholders.
• User adoption: Users will be stubborn in adopting new technologies and subtle visual/functional differences. To overcome this, provide training sessions about how to handle the visualization effects and the ways to handle them.

Best Practices for an AAS to Power BI Migration

1. Audit the existing AAS models, dependencies, and usage before starting the migration.
2. Choose the right Power BI capacity based on data volume and concurrency.
3. Validate DAX measures carefully to maintain business logic consistency.
4. Review the Power BI dataset for performance optimization. Reduce model complexity, optimize DAX measures enabling aggregation tables, and use appropriate storage modes to improve query response times.
5. Do user acceptance testing and validate the report performance.
6. Use shared datasets to promote security and governance.

8 Step AAS to Power BI Migration Process

1. Assess AAS environment:

Begin analyzing the current Azure Analysis service model that includes data sources, tables, relationships, DAX measures, partitions, and security roles. Ensure that the AAS server and the destination Power BI workspace are in the same Azure tenant. Make a backup configured in the AAS server and pointing to the Azure storage container.

2. Set up Power BI environment:

Power BI uses Azure Data Lake Storage Gen2 as a staging area. Connect the Power Bi workspace to an Azure Data Lake storage Gen2 (ADLS Gen2) account in the same tenant. Select the appropriate Power BI licensing models based on data volume, user concurrency, and refresh frequency. Ensure which AAS features are directly supported in Power BI datasets. 

3. Migration pairing:

The next step is to create the migration pair. This is required between the Azure server and the Power BI cloud. In the Power BI Service, start the migrations in Azure Analysis Services migrations. Authenticate and select the Azure subscription, Resource Group, and specific AAS server. Mention the Power BI Workspace where the models will stay and finalize the pairing.

4. Migrate semantic model:

Once the pairing is done, select the AAS databases to include and initiate the migration. Power BI starts copying the semantic models, data connections, DAX calculations, and security configurations from Azure Analysis Services (AAS) to Power BI datasets. Recreate tables, relationships, hierarchies, and DAX calculations within the Power BI dataset.

5. Security and Governance:

Verify Row-Level Security (RLS) and roles within Power BI. Ensure that workspace permissions, dataset access, and governance policies meet organizational security standards.

6. Validate reports:

Test the new Power BI dataset, such as data, relationships, measures, and performance. Conduct user acceptance testing to verify that business logic and KPIs remain consistent.

7. Redirection and Partitions: 

This is the most critical phase for minimizing user disruption. Redirect the server and route it towards the Power BI XMLA endpoint. Make the reports in the Power BI service point to Power BI semantic models. Set up data refresh schedules in Power BI and configure incremental refresh. Validate the partitions and refresh logic with performance and data freshness requirements.

8. Optimize and Go Live:

Apply performance tuning techniques such as model optimization, measure refactoring, and storage mode adjustments. Utilize Power BI features such as composite models and aggregation tables to improve query response times. Promote the dataset to production using deployment pipelines or controlled workspace access. Monitor refresh performance, query behaviour, and user adoption to ensure stability and reliability.

What to Do After You Migrate from AAS to Power BI

After a successful migration from AAS to Power BI, the following steps need to be taken to enter the stabilization and optimization phase. 

• Validation: Compare the key metrics, relationships, and DAX queries using Power BI Desktop live to the XMLA endpoint.
• Refresh datasets: Configure data refresh schedules and incremental refresh where applicable. Monitor refresh performance, failures, and capacity utilization to ensure data remains up to date and system resources are used efficiently. 
• Governance and compliance: Define standards for dataset ownership, naming conventions, workspace structure, and deployment pipelines. Strong governance helps maintain consistency, control changes, and support long-term scalability.
• Train users: Offer hands-on training for report developers and business users on Power BI features and self-service capabilities. A migration is considered successful only when the business users adopt the new Power BI-based analytics platform.
• Decommission Azure Analysis Services: Once the Power BI dataset is stable, start decommissioning the AAS instance. This will avoid unnecessary costs while completing the transition to a Power BI-centric analytics architecture.

Why Choose Entrans for Your AAS to Power BI Migration?

AAS to Power BI migration is a strategic shift towards modern business intelligence. Choosing a migration partner like Entrans will handle the complex dependencies, such as XMLA endpoints, ADLS Gen2 staging, and server redirection.

We bring deep expertise in both AAS and Power BI, proven migration accelerators with strong compliance towards governance policies. Entrans reduces the migration risks and ensures data and business logic consistency, and accelerates adoption.

Learn about how we make the AAS to Power BI migration into a foundation for sustained analytics growth. Book a consultation call.