Introduction

In today’s data driven world, seamless data migration is key to ensuring clean, reliable, and well structured insights across business systems.  

Whether you’re moving data from legacy systems, cloud platforms, or multiple sources, managing these transitions can be complex and time-consuming. 

Power BI Dataflows make it easier to clean, organize, and move data into Power BI, all without writing complicated code. They help make sure your reports and dashboards are built on accurate and consistent information no matter where your data is coming from. 

In this blog, we’ll dive deep into how to effectively use Power BI Dataflows for data migration, covering architecture, best practices, and optimization techniques to ensure your data is clean, consistent, and ready for analysis. 

What Are Power BI Dataflows? 

Power BI Dataflows are a cloud-based solution designed to streamline the process of preparing and transforming data. They allow users to connect to a variety of data sources such as SQL databases, Excel files, REST APIs, and more.

Using the Online Power Query Editor, users can clean, shape, and transform their data directly within the Power BI service eliminating the need for external tools or manual processing. 

Unlike Power BI Desktop, which focuses on report creation and visual analytics, Dataflows are created and managed within the Power BI service, typically inside a workspace.

These Dataflows can then be consumed in Power BI Desktop to build reports using consistent, centralized data. This separation of data preparation and visualization promotes better collaboration across teams and ensures data consistency throughout the organization. 

Why Use Power BI Dataflows for Data Migration?

Streamlining the Migration Process-

When moving data from different systems into Power BI, Dataflows offer a smart and efficient way to manage the process.

They help clean, organize, and prepare your data before it’s ever used in a report ensuring your dashboards are fast, reliable, and based on consistent information. 

Separating Data Prep from Reporting-

One of the biggest advantages of using Dataflows is separation of tasks. With Dataflows handling the heavy lifting of data transformation in the background, your report building process becomes much smoother.  

This separation not only boosts performance but also makes complex datasets easier to manage without slowing down your Power BI reports. 

Reusability and Centralized Control- 

Dataflows are also great for centralizing and reusing data. Instead of repeating the same data preparation steps across multiple reports or teams, you can set them up once and reuse them wherever needed.  

This not only saves time but also reduces errors and eliminates the need for developers to rebuild similar queries again and again. 

Smart Refresh Options- 

Another key benefit is automation and flexibility. You can set Dataflows to refresh your data on a schedule or update only the new data (instead of everything), which helps save time and system resources.  

Whether you’re working with large datasets or need fresh data daily, these features make sure your visuals are always up to date. 

Easy to Use and Fully Cloud-Based- 

Dataflows are cloud-based and easy to use, which means you don’t need to install extra tools or have a deep technical background.  

They support a wide range of data sources and are fully integrated with Microsoft tools like Power BI, Power Apps, and Power Automate making them a great fit for organizations using the Microsoft ecosystem for data migration and reporting. 

Step-by-Step Guide: Migrating Queries to Dataflows From Power BI Desktop to Dataflows

Migrating queries from Power BI Desktop to dataflows is straightforward:

1. Open Power Query Editor: In Power BI Desktop, open the queries you wish to migrate.
2. Copy Queries: Copy the M code of the query.
3. Create a Dataflow: In the Power BI service, create a new dataflow.
4. Paste Queries: Paste the M code into the dataflow’s online Power Query Editor.
5. Save and Refresh: Save the dataflow and perform a data refresh.

Migrating from Dataflow Gen1 to Gen2

Microsoft recently introduced Dataflow Gen2, offering enhanced performance and features. To migrate:

1. Assess Your Dataflows: Review existing dataflows for compatibility.
2. Plan the Migration: Leverage Microsoft’s migration guidance for a smooth transition.
3. Update Connections: Ensure reports point to the new Gen2 dataflows.

Also read: Canvas App vs. Model-Driven App: Choosing the Right Power Apps Solution

Best Practices for Data Migration with Power BI Dataflows

Empower Everyone with Self-Service Data Prep

Power BI Dataflows make it easy for non-technical users to clean and shape data on their own, without needing help from IT teams. Using a simple, visual interface like Power Query, business users can transform data without writing any code. This helps reduce IT workload and encourages more people to work with data confidently.

Optimize Data for Better Performance

To keep reports running smoothly, it’s important to design your dataflows efficiently. Use only the data you need, remove extra columns, and apply filters early. Enabling incremental refresh helps by updating only new data instead of everything saving time and improving speed, especially for large datasets. 

Automate Refreshes to Stay Up to Date

Your dashboards are only as good as your data. Setting up automated refresh schedules ensures your dataflows stay current without manual effort. Whether it’s daily, hourly, or weekly, automation keeps everything running in the background so reports always reflect the latest numbers.

Reuse Data Smartly 

Avoid doing the same work twice. With linked and computed entities, you can reuse cleaned-up data across multiple projects or reports. This saves time, reduces errors, and helps teams stay aligned. It’s especially useful in large organizations where the same data is used in different departments.

Keep Things Secure and Backed Up 

Don’t forget about security. Make sure only the right people have access to your dataflows and consider using row-level security if needed. Also, back up your workPower BI lets you export dataflows or automatically store them in Azure, which makes it easier to restore changes if needed.   

Top Questions About Power BI Dataflows

1. How do I migrate from Excel files to dataflows?

To migrate offline Excel files:

• Create a new dataflow.
• Import data from Excel files into the dataflow.
• Update Power BI reports to connect to the dataflow.

2. Can I export dataflow tables to CSV?

Yes, connect to the dataflow in Power BI Desktop, load the data into a table, and export it to CSV using Power BI’s export functionality.

3. What are the storage implications?

Dataflows use Azure Data Lake Storage, which ensures secure and scalable storage for large datasets.

4. Can I change a dataflow’s data source?

Yes, update the data source settings within the dataflow. Ensure the schema remains consistent to avoid breaking reports.

5. Why should I migrate to Dataflow Gen2?

Gen2 offers better performance, scalability, and integration features compared to Gen1. Migrating ensures you’re leveraging the latest capabilities.

Conclusion

Power BI dataflows simplify the process of data migration by providing a robust and scalable framework. By leveraging their capabilities, organizations can ensure consistent, efficient, and secure data management. Whether migrating from Excel files or upgrading to Dataflow Gen2, Power BI has the tools to streamline your data journey.

Ready to master dataflows? Explore Microsoft’s official documentation for detailed guidance.