Data, Dataflows and Migration in the Power Platform

The Role of Data in Power Platform

Within the Microsoft Power Platform ecosystem, data management lies at the heart of every business solution. Dataverse serves as the central repository for structured and relational information, enabling Power Apps, Power Automate, Power BI, and Power Pages to share and leverage consistent, integrated data. The true strength of the platform, however, emerges when addressing data migration and integration with external systems.

In enterprise projects, data migration is a critical phase that ensures that information from legacy systems is transferred to the modern platform while maintaining consistency, integrity, and traceability. Microsoft provides various tools to cover increasingly complex scenarios—from simple manual imports to advanced ETL pipelines based on Azure Data Factory.

Dataflows and Power Query

Dataflows are native tools available within the Power Apps maker portal that allow importing, transforming, and loading data from a wide range of sources using the Power Query transformation language. They represent a “no-code” solution ideal for low to medium-complexity migrations and continuous integrations between cloud systems.

• Import data from multiple sources such as SQL Server, Excel, Salesforce, SharePoint, or Dataverse itself.
• Automatic column mapping and “on-the-fly” Dataverse table creation.
• Data transformation capabilities through Power Query: filters, joins, pivots, merges, and custom calculations.
• Scheduling recurring executions to keep data synchronized.

A major advantage of Dataflows is the ability to migrate data between two Dataverse environments simply by configuring a source and target. This makes it easy to keep development, testing, and production environments aligned.

It’s important to note that by default, Dataflows support only cloud data sources. To connect on-premises sources, you must install the On-premises Data Gateway.

SQL Server Integration Services (SSIS) and Azure Data Factory

For complex data migration scenarios, Microsoft recommends enterprise-grade ETL tools like SQL Server Integration Services (SSIS) or Azure Data Factory (ADF). These tools handle high data volumes and sophisticated transformation processes.

SQL Server Integration Services

SSIS is ideal for on-premises or hybrid migrations. With dedicated connectors such as the KingswaySoft Integration Toolkit for Microsoft Dynamics 365, you can connect directly to Dataverse, Dynamics 365, and other Microsoft systems. Key capabilities include:

• Configurable extract, transform, and load (ETL) pipelines.
• Support for bulk data loading using ExecuteMultiple requests.
• Batch size, multithreading, and parallelism configuration to optimize performance.

Azure Data Factory

ADF is the cloud-native version of SSIS, designed for fully cloud-based migrations and integrations. An ADF pipeline consists of:

• Pipelines and Activities: define copy, transformation, or trigger operations.
• Datasets: represent data sources such as Azure SQL, Data Lake, Cosmos DB, or Dataverse.
• Dataflows: perform transformations like split, merge, pivot, filter, and sort.

A typical use case is copying data from Dataverse to Azure SQL Database for building data warehouses or analytical systems. For more details, visit Azure Data Factory documentation.

Synapse Link and Virtual Tables

Azure Synapse Link for Dataverse enables near real-time integration between Dataverse and Azure Synapse Analytics by replicating data into Azure Data Lake. This approach is ideal for advanced analytics and reporting with Power BI because data is automatically synchronized every few minutes.

Additionally, Virtual Tables allow displaying external data directly within Dataverse without physically importing it. This reduces storage impact while maintaining native access and relationships within model-driven apps.

• OData 4.0 support for external connections.
• Transparent access to external data within apps.
• Limitations on complex data types and features (e.g., auditing or offline mode).

These technologies enable hybrid architectures where only necessary data is migrated, while other data remains accessible on demand.

Data Migration Best Practices

Data migration is not just a technical task—it requires planning, governance, and attention to quality. Recommended best practices include:

• Accurate scoping: migrate only what is necessary and relevant to business processes.
• Data quality assessment: fix inconsistencies, duplicates, or missing fields before migration.
• Staging environment: consolidate and validate data before importing into Dataverse.
• Post-migration verification: compare records between source and target systems to ensure completeness.
• Automation: build repeatable pipelines for testing and re-importing data.

It’s also essential to evaluate the impact on Dataverse storage and consider purchasing capacity add-ons if the expected data volume exceeds available space. Finally, ensure compliance with data residency and privacy regulations, especially for international projects.