TL;DR
For most mid-market companies, partnering with a Power BI consulting firm often costs less and delivers faster than building an in-house BI team, which can take months to hire and cost hundreds of thousands in its first year. Building in-house makes sense at large enterprise scale, with highly regulated data, or when analytics is a long-term differentiator. Many organizations start with a partner, then move selected work in-house.
Dataflow Gen1 Retirement – What It Means and How to Prepare
Microsoft has officially announced the retirement of Power BI Dataflow Gen1, marking a significant transition in the Power BI and Microsoft Fabric ecosystem. This change is part of a broader strategy to modernize data integration and unify experiences under Microsoft Fabric.
If you are currently using Dataflow Gen1, it’s crucial to understand what this retirement means and how to prepare for the future.
What is Dataflow Gen1?
Dataflow Gen1 is a feature in Power BI that allows users to perform ETL (Extract, Transform, Load) operations using Power Query Online. It enables reusable data transformation logic and centralized data preparation in the cloud.
However, with evolving needs and the introduction of Microsoft Fabric, Gen1 is being phased out in favor of a more advanced solution.
Why is Dataflow Gen1 Being Retired?
Microsoft is retiring Dataflow Gen1 to:
- Provide a unified data platform through Microsoft Fabric
- Improve performance and scalability
- Enable advanced features like CI/CD, Git integration, and better monitoring
- Reduce fragmentation across tools
The new approach focuses on Dataflow Gen2, which is built on Fabric and offers significantly enhanced capabilities.
What is Dataflow Gen2?
Dataflow Gen2 is the next-generation data integration tool designed for Microsoft Fabric. It brings several improvements over Gen1:
1. Enhanced Performance
Built on modern infrastructure, Gen2 handles larger datasets more efficiently.
2. Integration with Fabric
Seamlessly connects with Lakehouse, Warehouse, and other Fabric components.
3. Advanced Monitoring
Provides better visibility into refreshes, failures, and performance metrics.
4. CI/CD and Version Control
Supports Git integration for better development lifecycle management.
5. Improved Data Lineage
Offers clearer insights into data flow and dependencies.
Key Considerations for Migration
If you are using Dataflow Gen1, here’s what you should do:
1. Assess Existing Dataflows
Identify all Gen1 dataflows and understand their dependencies.
2. Plan Migration Strategy
Decide whether to rebuild or migrate dataflows to Gen2.
3. Test Thoroughly
Ensure data accuracy and performance after migration.
4. Update Dependencies
Check reports, datasets, and dashboards that rely on these dataflows.
5. Train Your Team
Familiarize your team with Microsoft Fabric and Dataflow Gen2.
Migration Approach
There are two common approaches:
- Lift-and-Shift: Recreate existing logic in Gen2
- Modernization: Redesign dataflows to leverage Fabric capabilities
While lift-and-shift is quicker, modernization provides long-term benefits.
Benefits of Moving to Gen2
- Better scalability and performance
- Unified data platform
- Advanced development practices (CI/CD)
- Improved governance and monitoring
- Future-ready architecture
Challenges During Transition
- Learning curve for Microsoft Fabric
- Potential rework of existing logic
- Dependency management
- Licensing considerations
Proper planning can help mitigate these challenges.
The Bigger Picture
The retirement of Dataflow Gen1 is not just a deprecation, it’s a step toward a more integrated and powerful data ecosystem. Microsoft Fabric aims to bring together data engineering, data science, real-time analytics, and business intelligence into a single platform.
Conclusion
While the retirement of Dataflow Gen1 may seem disruptive, it presents an opportunity to modernize your data architecture. By adopting Dataflow Gen2 and Microsoft Fabric, organizations can unlock new capabilities and stay ahead in the data landscape.
Start planning your migration today to ensure a smooth transition and take full advantage of what the future holds.
FAQs
Microsoft has moved Power BI Dataflow Gen1 into a legacy state. It will no longer receive new feature investments, and Microsoft is directing customers toward Dataflow Gen2. Existing Gen1 workloads can continue to run while organizations plan their transition.
Microsoft has announced a shift away from Gen1, but legacy status does not mean immediate discontinuation. Existing workloads remain supported. Organizations should monitor Microsoft’s official announcements for future retirement timelines and migration guidance.
Dataflow Gen1 is designed mainly for Power BI data preparation, while Gen2 is built for the broader Microsoft Fabric ecosystem. Gen2 offers multiple data destinations, improved monitoring, pipeline integration, better scalability, and CI/CD capabilities.
Dataflow Gen2 is a cloud-based data integration tool within Microsoft Fabric. It uses Power Query to connect, clean, and transform data from different sources. It can load transformed data into destinations such as Fabric Lakehouse, Warehouse, Azure SQL, and other supported platforms.
You can migrate using several approaches:
- Use the Dataflows Upgrade Wizard (currently in preview).
- Use the Save As feature where supported.
- Export and import Power Query templates.
- Copy and paste existing Power Query logic.
Before migrating, review dataflow dependencies, refresh settings, connectors, and downstream reports.
Not necessarily. Most downstream semantic models and reports can continue working after a successful migration. However, some connections, legacy connectors, DirectQuery configurations, and refresh settings may require changes. Test all dependent reports before completing the migration.
Dataflow Gen2 requires Fabric capacity, Fabric trial capacity, or Power BI Premium capacity. A Fabric Free license can be sufficient to author Dataflow Gen2 in a Premium capacity workspace. Licensing requirements depend on the workspace and capacity configuration.
Yes. Microsoft currently allows Pro and Premium Per User (PPU) customers to continue using Dataflow Gen1. Guidance for Gen2 adoption in these licensing scenarios is evolving. Existing users should follow Microsoft’s latest licensing and migration announcements.
Dataflow Gen2 provides several benefits:
- Better performance and scalability.
- Multiple data destinations.
- Integration with Microsoft Fabric pipelines.
- Improved refresh monitoring.
- CI/CD and Git integration.
- Better support for enterprise data workflows.
These capabilities help organizations build more reusable and manageable data integration solutions.
Scheduled refresh configurations may be preserved when using the in-place Upgrade Wizard. However, refresh behavior and certain settings, particularly incremental refresh, may need additional configuration. Always validate refresh schedules and run test refreshes after migration.
Common migration challenges include:
- Identifying downstream dependencies.
- Updating unsupported connectors.
- Reconfiguring incremental refresh.
- Reviewing data destination settings.
- Managing licensing and Fabric capacity.
- Validating data accuracy and report performance.
A structured migration plan helps reduce disruption and avoid unexpected reporting issues.
Yes. Dataflow Gen2 supports incremental refresh, allowing organizations to process changed data instead of refreshing the entire dataset. However, Gen1 incremental refresh settings are not automatically carried over during the in-place upgrade. You must review and configure the required settings in Gen2.
Organizations using Fabric or Premium capacity should start assessing their Gen1 workloads and plan a transition to Gen2. Teams using Pro or PPU can continue with Gen1 while monitoring Microsoft’s evolving guidance. Early planning helps identify dependencies, evaluate licensing, and reduce migration risks.
Author By
Kamal Sharma
Kamal brings over 20 years of experience in data analytics and business intelligence. He has led the design and implementation of analytics solutions across operations, financial reporting, and performance improvement initiatives. With a background in business statistics and Six Sigma, his work focuses on applying data in a structured and practical way to solve real business challenges.
