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MIGRATION OF DATA FROM SHAREPOINT LIST TO DATAVERSE TABLE USING DATAFLOW



In today's data-driven world, businesses generate and handle vast amounts of data daily. For effective data management, organizations often employ powerful platforms like SharePoint and Dataverse (formerly known as Common Data Service) within the Microsoft ecosystem. SharePoint facilitates seamless collaboration and document management, while Dataverse offers a robust database solution for structured data storage.

In this short guide, we will explore the step-by-step process of migrating data from a SharePoint List to a Dataverse Table using Dataflow. We will also highlight the benefits of adopting Dataflow for this migration, such as reduced manual effort, improved data quality, and enhanced data governance.

By the end of this guide, you will have a clear understanding of how to seamlessly migrate data from SharePoint to Dataverse using Dataflow, empowering your organization with a more efficient and integrated data management solution.

STEP 1: Open Power Apps and Select Dataflows from the left panel. Click ‘New dataflow’ to create a new dataflow.



STEP 2: Give a name to your dataflow and click ‘Create’.



STEP 3: From the list of Datasource available select ‘SharePoint Online list’.



STEP 4: At this point we need to provide the site address of the site which contains the SharePoint list we want to migrate data from. The next step is to create a connection if not present already. Since we have a connection already, we will select it from the option bar and click ‘Next’.



STEP 5: Clicking on ‘Next’ will open this page which will need us to select the required SharePoint list from the list of all the SharePoint lists this site consists of.



STEP 6: After selecting the required list, we will be able to see the Columns and the data in a table format. Click ‘Transform Data’.



* Dataflows enable us to transform the structure and content of our data to make it more suitable for analysis and reporting. We can use the "Transform data" step to reshape the data, rename columns, split or combine columns, change data types, apply formulas or functions, and perform other data transformations as needed.


STEP 7: The next step is to choose the necessary columns we require data to be migrated from. Click ‘Choose Columns’.



Select the required columns and click ‘Ok’:



We can change the column type by selecting individual columns.

Click ‘Next’ once changes are done.



STEP 8: Now we can perform the action based on our requirement which in our case is to load data into an existing Dataverse table. Select the ‘Destination Table’ from the options provided and map the respective columns in the list to the table I.e., Source Column to Destination Column as shown below.

Click ‘Next’ -> ‘Publish’ to save and publish the created dataflow.



Now, we can see the Refresh status of our dataflow which will change from in progress until the data is fed into the destination table:



Click on the ellipsis menu and select ‘Show refresh history’ to see if the dataflow operated successfully or any error prevails.




Our dataflow operated successfully which implies data has been successfully migrated into the Dataverse table from the SharePoint list.



The Destination Dataverse table with the data migrated successfully:



In conclusion, the migration of data from a SharePoint List to a Dataverse Table using Dataflow offers a seamless and efficient data management solution for businesses. By following the step-by-step guide provided, organizations can easily transfer data, reducing manual effort, improving data quality, and enhancing data governance. Dataflows empower users to transform the data structure, making it suitable for analysis and reporting, and the process ensures that the data is fed accurately into the destination table. With this successful migration, businesses can harness the power of Dataverse's structured data storage, enabling better integration and collaboration within the Microsoft ecosystem. Embracing Dataflow for data migration empowers organizations to leverage their data effectively, promoting better decision-making and driving success in today's data-driven world.




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