Migrating data between systems can be a complex process, especially when dealing with large datasets, different data structures, and multiple integrations. Power Automate simplifies data migration by enabling seamless data transfer between cloud services, databases, and on-premises systems.
In this article, we’ll cover:
Why use Power Automate for data migration?
Common use cases for data migration
Step-by-step guide to migrating data
Best practices for a smooth migration
1. Why Use Power Automate for Data Migration?
No-Code/Low-Code Solution – Ideal for non-developers and IT professionals alike.
Supports Multiple Data Sources – Integrates with SQL, Dataverse, SharePoint, Excel, APIs, and more.
Automated & Scheduled Transfers – Reduces manual effort and ensures data consistency.
Error Handling & Logging – Ensures data integrity during migration.
Cloud & On-Premises Support – Works with both Microsoft and third-party platforms.
2. Common Use Cases for Data Migration
✔️ Migrating from SharePoint On-Premises to SharePoint Online
✔️ Transferring data from SQL Server to Dataverse
✔️ Syncing legacy systems with modern cloud platforms
✔️ Moving Excel or CSV data to a database
✔️ Migrating CRM data between Dynamics 365 and Salesforce
✔️ Automating backups from one system to another
3. Step-by-Step Guide to Data Migration with Power Automate
Step 1: Identify Data Sources & Destination
Before starting, determine:
✔️ The source system (e.g., SQL Server, SharePoint, Dataverse).
✔️ The destination system (e.g., Azure SQL, Dynamics 365, Power BI).
✔️ Data format compatibility (e.g., JSON, CSV, XML).
Step 2: Create a New Power Automate Flow
1️⃣ Go to Power Automate – Power Automate Portal
2️⃣ Click “Create” > “Automated Cloud Flow”
3️⃣ Choose a Trigger Based on Data Source
Example Triggers:
✔️ “When an item is created” (for SharePoint or Dataverse)
✔️ “When a row is modified” (for SQL Server)
✔️ “When a file is created in a folder” (for OneDrive or SharePoint)
✔️ “Recurrence” (for scheduled migrations)
Step 3: Retrieve Data from the Source System
After setting up a trigger, fetch the data you want to migrate.
Example Actions:
✔️ “Get rows” (Dataverse, Excel, SQL Server)
✔️ “List records” (Dynamics 365)
✔️ “List items” (SharePoint)
Tip: Use Filter Queries to retrieve only relevant data and improve efficiency.
Step 4: Transform Data (Optional)
Sometimes, the data structure from the source does not match the destination. Use:
✔️ “Select” Action – Format the data before inserting it into the new system.
✔️ “Compose” Action – Modify data fields using expressions.
✔️ Power Query – Clean and reshape data before migration.
Example: Transforming Data from SharePoint to Dataverse
- Convert a date format from
dd/MM/yyyytoyyyy-MM-dd. - Rename columns before inserting them into Dataverse.
Step 5: Insert Data into the Destination System
Once data is formatted correctly, insert it into the target system.
Example Actions:
✔️ “Add a row” (Dataverse, Excel, SQL Server)
✔️ “Create an item” (SharePoint)
✔️ “Insert row” (Google Sheets)
Tip: If migrating large datasets, use batch processing to avoid performance issues.
Step 6: Handle Errors & Logging
During data migration, errors may occur due to format mismatches, API limits, or connectivity issues.
✔️ Use “Scope” and “Configure Run After” to manage errors.
✔️ Store error logs in Excel, SharePoint, or Dataverse.
✔️ Send email alerts using “Send an Email” action when a flow fails.
Example: Logging Failed Records
- If data insertion fails, save the failed record in an “Error Log” table.
- Notify the IT team about the issue.
4. Best Practices for a Smooth Data Migration
✅ Test with Sample Data – Run a test migration with small datasets before full execution.
✅ Use Pagination for Large Data Sets – Fetch records in batches to avoid API limitations.
✅ Optimize Flow Performance – Use parallel execution where possible.
✅ Keep Backup Copies – Store original data before migration.
✅ Monitor Flow Execution – Regularly check the Power Automate Run History for issues.
5. Real-World Use Cases
Example 1: Migrating Customer Data from SQL to Dataverse
Problem: A company using SQL Server wanted to migrate customer records to Dataverse.
Solution:
✔️ Power Automate extracted customer data from SQL.
✔️ Data was transformed (standardized phone numbers & dates).
✔️ The final data was inserted into Dataverse tables.
Result: Automated, seamless migration with zero manual intervention.
Example 2: Migrating Files from SharePoint to Azure Blob Storage
Problem: A business needed to move documents from SharePoint to Azure.
Solution:
✔️ Power Automate triggered when a new file was uploaded.
✔️ The file was copied to Azure Blob Storage.
✔️ A confirmation email was sent to users.
Result: Efficient, automated file migration with minimal effort.