Dataverse: Data Migration Tools

Overview of Data Migration in Dataverse

Data migration is one of the most delicate phases in implementing a Microsoft Power Platform solution. Bringing information from legacy systems or heterogeneous databases into Microsoft Dataverse requires accurate planning, the right technology choices, and reliable tools. The platform offers several options for executing migrations of varying complexity, from small manual imports to enterprise-scale ETL processes with cloud orchestrations.

This guide explains in detail the main tools referenced in official Microsoft documentation and architectural best practices: Excel import, Configuration Migration Tool, SQL Server Integration Services (SSIS), and Azure Data Factory (ADF). Each solution addresses specific scenarios in terms of data volume, automation, governance, and security.

Importing Data via Excel

Excel import is the most direct and accessible way to load data into Dataverse. It is recommended for small volumes or occasional table updates, particularly during testing or initial configuration of sandbox environments.

Within a model-driven app, the Export to Excel option using a “Static Worksheet” file type allows you to generate a file that includes hidden metadata in the first columns, useful for re-importing. Users can modify or add rows in the spreadsheet and reimport it directly into Dataverse while maintaining record consistency.

• Supports XML, CSV, TXT, and XLSX formats.
• 8 MB limit per file, or 32 MB for multi-table ZIPs.
• Recommended for up to 20,000 records per import.
• Ideal for demo environments or light configuration migrations.

For programmatic use, the import wizard can be invoked via Dataverse APIs, enabling automation and custom mapping. More details are available in the official Microsoft documentation.

Configuration Migration Tool

The Configuration Migration Tool is designed to transfer small amounts of configuration data between Dataverse environments. It is part of the development tool package and is frequently used to accelerate the migration of reference or system parameter tables.

The migration process follows three steps:

1. Select the source environment and define the schema (tables and columns) to export, saved as an XML file.
2. Create the ZIP file containing the exported data according to the schema.
3. Import the ZIP package into the target environment.

The tool allows you to:

• Filter records for migration.
• Preserve relationships between related tables.
• Validate schema consistency.
• Map users between environments.

You can also include exported files in the Package Deployer to automate loading during managed solution installations. This approach is useful for ISV or continuous deployment scenarios.

SQL Server Integration Services (SSIS)

For complex migration scenarios, Microsoft recommends SQL Server Integration Services (SSIS), a powerful ETL tool capable of managing large data volumes and advanced transformations. With SSIS, you can create a staging database, consolidate data from multiple sources, and finally load it into Dataverse.

The reference solution uses the KingswaySoft SSIS Integration Toolkit for Dynamics 365 and Dataverse, a product widely adopted by the expert community. This connector allows connections to both Dataverse and Dynamics 365 ERP/CRM systems, facilitating mapping and transformations.

The typical SSIS flow includes three main phases:

• Extract: Import from legacy databases via Linked Servers or SSIS connectors.
• Transform: Consolidate and cleanse data in the staging database, restoring relationships and ownership.
• Load: Final load into Dataverse using the KingswaySoft connector, with batch and multi-threading options for performance optimization.

For large migrations, it is recommended to run SSIS on an Azure Virtual Machine in the same region as the Dataverse tenant, reducing latency during API calls.

Azure Data Factory (ADF)

Azure Data Factory (ADF) is the cloud-native solution for enterprise data migration and integration. It is best suited for “cloud-only” scenarios and for orchestrating complex pipelines in enterprise environments.

ADF is based on key components:

• Pipelines and Activities: define copy, transformation, or Azure Function invocation flows.
• Datasets: represent data sources and destinations (Azure SQL, Dataverse, Data Lake, etc.).
• Dataflows: manage advanced transformations (merge, pivot, split, filter, sort).

A common example is a pipeline that copies data from Dataverse to Azure SQL or vice versa, with column mapping and quality control. Pipelines can be automated for scheduled or triggered executions.

ADF complies with Microsoft security principles and uses native Dataverse connectors registered in Azure Active Directory. Learn more in the official Azure Data Factory documentation.

When to Choose Each Tool

Tool	Ideal Scenario	Data Volume	Automation
Excel Import	Testing, demos, quick configuration	Low (<20,000 records)	Manual
Configuration Migration Tool	Configuration between environments	Low/Medium	Partial
SSIS + KingswaySoft	Enterprise on-premises or hybrid projects	High	Full (ETL)
Azure Data Factory	Cloud migrations and complex orchestrations	High	Full (Cloud ELT)