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

When large datasets meet business scalability requirements, potential performance issues arise. This is the case if you are still standing up the Looker analytics platform. Many businesses are now transitioning to Power BI. They aim to benefit from Microsoft ecosystem integration, improved visualization, and reduced costs. Migrating from Looker to Power BI offers richer dashboards. It also creates a flexible and scalable analytics environment. 

This post will guide you through the process of upgrading your analytics platform. It will also outline the steps for a smooth and seamless Looker to Power BI migration.

What Makes the Looker to Power BI Migration Beneficial?

Migration from Looker to Power BI is beneficial as it unlocks the user-friendly interface for a broader user base and lowers the Total Cost of Ownership (TCO). The key benefits of Looker to Power BI Migration are

• Cost reduction: Power BI gives a more cost-effective solution than Looker. So, Power BI migration can reduce the Looker licenses and support costs. This will assure long-term growth.
• Integration Ecosystem: Power BI natively integrates with Microsoft 365, Azure, Dynamics 365, SQL Server, and other data connectors. Through this feature, Power BI migration will eliminate the need for complex, third-party connectors and APIS to share reports.
• User-friendly interface: Power BI’s user-friendly drag-and-drop interface enables business users to customize dashboards. Thus, they reduce the learning curve for non-technical users. 
• Data Modelling: Power BI uses a Semantic layer based on relationships and DAX (Data Analysis Expressions) for complex calculations. As DAX is widely used, it makes business analysts adapt to Power BI rather than learning a new language like LookML.
• Data governance: Power BI uses Azure Active Directory (AAD) for identity management and offers advanced features like Row-Level Security (RLS), which aligns with enterprise-level security.
• Visualization: Power BI offers richer visuals and extensive control over customization. Through their drag-and-drop interface, they make it easy for even non-technical business users.

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

Both Looker and Power BI differ in their architecture and modeling languages. Some key challenges and ways to overcome them are

• Layout differences: Looker’s visualizations and dashboard layouts are different from Power BI. Recreating dashboards in Power BI can be challenging. To overcome this, check whether there is a direct equivalent visual in Power BI. Only migrate essential reports to Power BI and archive the old ones.
• Logic translation: The most challenging part of the Looker to Power BI migration is translating the business logic from LookML into DAX and Power Query. Looker uses MookML modelling language, whereas Power BI uses Power Query for Data transformation/ETL and DAX for complex calculations. To handle this, thoroughly audit the LookML models. Determine where the logic is to be handled, whether it is in a data warehouse, Power Query, or DAX. Now convert the LookML logic into DAX measures using the translator framework. Even simpler Looker calculations require complex DAX formulas.
• Data consistency and validation: Data validation is more important than others. Ensure data parity between Looker and Power BI. To handle this, before starting to migrate, document the data transformations in the Looker platform. Take key metrics and run SQL scripts simultaneously in Looker and Power BI and compare the results. 
• User adoption and training: Customers who are used to Looker’s dashboard may find it difficult to adapt to Power BI’s interface. To overcome this, provide comprehensive targeted training sessions, provide documentation, and offer hands-on workshops.
• Performance optimization: Looker typically uses DirectQuery by sending all queries to the cloud data warehouse. Large data sets can lead to performance issues. To overcome this, use a star schema to optimize data models, aggregate tables, and incremental data refresh in Power BI. For smaller data sets, use Import mode where only real-time data or extreme data volume is required. Ensure that the users are comfortable with the new Power BI tool before the full rollout.
• Security and Access control: Looker Row Level Security(RLS) is handled by user attributes, whereas Power BI RLS is defined using DAX filters on tables. To overcome this, document existing Looker RLS logic and rebuild it using Power BI’s DAX filter expressions. Then perform testing with user groups before deploying it in production.

Best Practices for a Smooth Looker to Power BI Migration

A clear road map of how to proceed with the Looker to Power BI migration can become a complex and time-consuming process. Below are the best practices for achieving a smooth Looker to Power BI migration.

• Do a report usage analysis to identify the high-value and frequently used ones and start migrating.
• Conduct an inventory analysis on all Looker assets, classifying report data sets.
• Start with pilot projects and migrate gradually. 
• Translate Looker’s LookML code into certified Power BI semantic models using star schema models.
• Use a phased approach to transfer critical data.
• Before deployment, perform side-by-side comparisons between Looker and Power BI dashboards.
• Provide training sessions to help users understand Power BI features.
• Establish governance policies to strengthen security.

8 Step Looker to Power BI Migration Process

Migration from Looker to Power BI involves a structural process that requires careful planning and rebuilding of existing assets. Below is the step-by-step migration process that helps us in a seamless and smooth transition

1. Assessment and planning: Analyze the Looker environment and create a list of Looker reports, data models, data sources, user groups, and sources. Check their Looker usage logs to identify which reports are critical for business operations. Establish a governance model in Power BI using your existing model. Determine where to store original Power BI desktop files and create a roadmap. This roadmap clearly says about deployment strategy, budget, timelines, and available resources.
2. Power BI environment setup: Enable the Power BI connector in Looker’s BI Connectors page. Configure Power BI Desktop security to trust the connector. Connect to Looker data using DirectQuery mode to facilitate large dataset queries. Define the Power BI architecture, data flow, and governance model.
3. Rebuild data models: Create a pilot project (PoC) and start recreating LookML models and dashboards. Select the LookML structure that includes joins and Explores to Power Query transformations and data model relationships. Recreate the logic in Power BI using Power Query - M language for data transformation and DAX for complex calculations. Calculate corresponding DAX measures with the Looker’s table. Apply modeling best practices such as star schema, proper cardinality, and summarization tables. 
4. Migration execution: Based on the PoC findings, start migrating the rest of the LookML models into Power BI semantic data models. Recreate the prioritized list of Looks and Dashboards as Power BI reports. Use Power BI’s Power Query to perform data transformations, documenting your steps and reusing queries where possible. Set up new schedules in Power BI for any reports that need regular delivery.
5. Rebuilding dashboard: Recreate your Looker dashboards and visuals in Power BI. Keep the bookmarks, drillthroughs, filters, and interactions as such to preserve user experience.
6. Security, compliance, and access controls: Implement Row-level security (RLS) and other security measures defined in the LookML model within the Power BI security framework. Integrate Azure AD groups and conditional access if required. Maintain documentation and logs for governance and regulatory compliance. Ensure security data is not exposed.
7. Testing and Quality assurance: Once all the reports are deployed, ensure all filters, drill-downs, and visualizations work as expected in Power BI. Perform side-by-side validation for each migrated Explore and dashboard. 
8. Validation and deployment: Verify metrics, KPI outputs, and aggregation logic between Looker and Power BI. Now start deploying the Power BI datasets and dashboards in a phased deployment via Power BI pipelines. Continuously monitor Power BI usage and performance.

Things to Be Considered Post Looker to Power BI Migration

After a successful Looker to Power BI migration, post-maintenance support is necessary to maintain stability, data accuracy, user adoption, and long-term governance.

• User Acceptance Testing (UAT): Perform a thorough data validation of Looker and Power BI to ensure data integrity and accuracy. Execute sample reports and key metrics side-by-side to verify that calculations, aggregations, and values match. Ask the business users to do data accuracy checks by measuring KPIs and validating reports.
• Monitor and optimize: Track Power BI usage metrics, performance bottlenecks, and error rates, adjusting resource allocations and report design as required.
• User Adoption: Provide focused training for users and analysts about Power BI’s DAX and visualization frameworks. End-users need to understand navigation and consumption, while analysts need to master DAX Power Query and Power BI Desktop best practices.
• Decommission Looker: After the new Power BI platform is stable and users have adopted it. Decommission the Looker environment to finalize the transition and obtain the full potential of Power BI.

How can Entrans help with Looker to Power BI Migration?

The shift from Looker to Power BI is not an upgrade; it is a strategic move towards future-proofing. Power BI offers a user-friendly interface, real-time insights, and seamless collaboration with Microsoft 365.

Partnering with a team of migration experts like Entrans takes away the risks that come with manually migrating from Looker to Microsoft Power BI. Our technical team has deep knowledge of both Looker and Power BI. From pre-migration planning to post-migration optimization, we ensure a seamless transition and manually rebuild difficult layouts and securely move all your content and images. 

Want to know more? Book a consultation call today with no obligation!.

Frequently Asked Questions (FAQs):

1. What is the Looker to Power BI migration?

Looker to Power BI migration is a process of transferring all the organization's business assets, such as data models and reports, from the Looker platform to Microsoft Power BI. This involves converting LookML data models and views into Power BI data flows, DAX, and recreating dashboards.

2. Can all Looker dashboards be replicated in Power BI?

Most Looker dashboards can be visually recreated in Power BI, but some features, like perfect feature-for-feature parity, are not guaranteed. Others may require manual recreation due to platform differences.

3. How can we ensure data accuracy between Looker and Power BI?

After the Looker to Power BI migration, data accuracy can be ensured by validating data sets, aligning data models, and performing cross-platform reconciliation. Comparison should be performed by running identical reports or queries in both platforms and comparing the results.

4. Is there any automated tool to migrate from Looker to Power BI?

No, there is no fully automated migration tool for Looker to Power BI migration. This will not be feasible due to semantic and architectural differences between the two BI tools.