Power Apps: How to Turn Excel Spreadsheets into Power Apps (2025

Key insights

• Excel prep: Format each data set as a proper table and save the file to cloud storage like OneDrive for Business or SharePoint so Power Apps can access it easily.
  Keep column names clear and remove blank rows or headers inside tables.
• Import into Power Apps: Use the "Start with data" option to upload your Excel file; Power Apps parses tables and creates corresponding Dataverse tables automatically.
  Check that column types and lookup/mapping suggestions match your intent before importing.
• Use Copilot and AI: AI analyzes your Excel schema and suggests table relationships, lookup columns, and an initial app layout.
  It can scaffold either Canvas Apps or Model-driven apps to speed up development.
• Build and customize: Power Apps generates galleries, forms, and search/filter features you can edit in Power Apps Studio.
  Add Excel-like formulas, adjust layouts, and embed business logic to match workflows.
• Test and publish: Preview the app on desktop, tablet, and mobile, then save and publish it to your organization.
  Apply performance optimizations for large datasets and verify lookup relationships after import.
• Benefits and best practices: The no-code/no-code/low-code approach speeds app delivery and uses Dataverse for relational data safety and governance.
  Keep tables clean, model multi-table relationships explicitly, and store source files in cloud locations for reliable syncing.

Overview

In a recent YouTube tutorial, Reza Dorrani demonstrates how to convert Excel spreadsheets into functional apps using Microsoft Power Apps in 2025. The video walks viewers from initial setup through publishing, highlighting both Canvas and Model-driven approaches and how data moves into Dataverse. Importantly, the tutorial emphasizes AI assistance with Copilot, which can scaffold tables, map relationships, and generate interfaces automatically. Overall, the video aims to help beginners and experienced users speed up app creation while keeping data integrity in mind.

What the Video Demonstrates

First, Dorrani shows how to prepare an Excel file by structuring it as formal tables and storing the file on cloud storage such as OneDrive or SharePoint. Next, he uses the Power Apps "Start with data" workflow to import those tables and convert them into Dataverse tables, walking through mapping lookup columns and establishing relationships. Then the tutorial builds a working app skeleton with galleries, forms, and search functionality, and it explains how to preview the app on desktop and mobile devices. Finally, the video covers how to save, test, and publish the app so teams can use it immediately.

In addition to basic import steps, Dorrani tackles more complex scenarios such as multi-sheet workbooks, pivot tables, and Excel-derived logic. He explains how the system interprets relationships across sheets and how to create formula columns that mirror Excel logic inside Dataverse. Moreover, the demonstration includes examples of building a Model-driven app with interactive pages, showing the benefits for enterprise-style data handling. The walkthrough also highlights the built-in agents and custom page experiences that improve usability for end users.

AI and New 2025 Enhancements

Significantly, the 2025 workflow integrates Copilot to automate the most time-consuming tasks, such as generating table schemas and suggesting relationships. This AI support reduces setup time by interpreting column names, detecting likely lookup fields, and proposing app layouts, which accelerates development for non-developers. However, Dorrani stresses that AI suggestions still need human review, since automated mappings can misinterpret ambiguous headers or complex formulas. Therefore, users should validate generated tables and relationships before committing to production apps.

Furthermore, Power Apps now better supports multi-table imports and relational data, allowing more sophisticated Model-driven applications to arise directly from spreadsheets. The video demonstrates how to import data with lookup columns and keep relationships intact, which helps migrate larger, more structured Excel systems into a scalable backend. Additionally, tutorials in the video advise on performance optimization for larger datasets and show how to structure data to avoid slow app behavior. Consequently, these enhancements make the tool more suitable for enterprise scenarios while still requiring careful planning.

Tradeoffs and Challenges

Despite the clear benefits, the conversion process presents tradeoffs, particularly around complexity versus speed. On one hand, automating schema creation brings speed and lowers entry barriers, but on the other hand, it can obscure subtle data-quality issues that Excel users had tacitly managed. For example, pivot tables and implicit Excel logic may not translate directly, forcing additional cleansing or redesign of formulas inside Dataverse. Thus, teams must weigh the convenience of rapid generation against the effort needed for model validation and cleanup.

Another significant challenge involves costs and governance. Storing data in Dataverse and using production-grade features can incur licensing and capacity implications that organizations must plan for. In addition, access control and security practices change when you move from a single Excel file to a managed data platform, so IT teams need to define roles and sharing policies. Therefore, while the tutorial shows a fast path to app creation, organizations must consider long-term maintenance, cost, and compliance when adopting this approach.

Practical Tips and Final Takeaways

To get the best results, Dorrani recommends formatting data as tables, keeping column names clear and consistent, and using cloud storage for seamless import. He also advises running the AI-assisted import in a sandbox environment first, then validating mappings, relationships, and calculated columns before switching to production. Moreover, the tutorial suggests optimizing data models by normalizing repeating information and avoiding overly large flat tables to improve app responsiveness. In short, careful preparation and review help balance speed with reliability when converting spreadsheets into apps.

In conclusion, Reza Dorrani’s video offers a practical, step-by-step guide for turning Excel workbooks into working Power Apps in 2025, while highlighting the new capabilities of AI-assisted conversion and relational imports. It provides a realistic picture of both the time savings and the potential pitfalls, urging users to validate AI results and plan for governance and cost. Consequently, the tutorial serves as a useful starting point for teams that want to modernize spreadsheet-based processes with low-code tools and a scalable backend. Finally, readers who follow the recommended preparation steps should find the workflow both accessible and powerful for a wide range of use cases.

Keywords

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