Three Major Approaches for Data Integration into the Dataverse | Microsoft Power Platform Solution Architect Exam - PL-600

Correct Answers: B, C and E

Dataverse provides several tools to import and export data.

They include Data Import Wizard, Power Query, Power Automate, Dataflows, and others.

A Solution Architect always needs to assess when to bring data into the Dataverse or connect to an external data source.

Dataverse can integrate various data types from multiple sources.

There are three major approaches to import data into and integrate data with the Dataverse: Scheduled integration with other systems - users can sync data from other sources into Dataverse and schedule it.

Transform and import data using Power Query - Power Query helps extract and transform data for import into Dataverse.

Dataflows use Power Query for data connectivity, preparation, and transformation, and then load data into the Dataverse, Power BI workspace, or Azure Data Lake storage account.

Data import - users can use a simple one-time (or occasional) data import from Excel or CSV files.

Option A is incorrect because the Dashboards can present data, but it does not import data into the Dataverse.

Option D is incorrect because Power BI datasets result from the data import or integration, but it does not import data into the Dataverse.

For more information about Dataverse data integration, please visit the below URL:

Sure, I'd be happy to provide a detailed explanation of the three major approaches for data integration into the Dataverse that can help your design:

1. Data import: The first major approach for data integration into the Dataverse is data import. This approach involves importing data from external sources into the Dataverse using tools such as the Power Platform Data Import Wizard. This tool allows you to map the fields in the external data source to the fields in the Dataverse and import the data in bulk. This approach is useful when you need to bring in a large amount of data from an external source and want to automate the process.

2. Transform and import data using Power Query: The second major approach for data integration into the Dataverse is to use Power Query to transform and import data. Power Query is a data transformation and cleansing tool that allows you to extract data from multiple sources, transform it, and load it into the Dataverse. This approach is useful when you need to clean and transform data before importing it into the Dataverse. Power Query can be used to merge, filter, and transform data from multiple sources before importing it into the Dataverse.

3. Scheduled integration with other systems: The third major approach for data integration into the Dataverse is scheduled integration with other systems. This approach involves setting up scheduled data integration jobs between the Dataverse and external systems using tools such as Power Automate. This approach is useful when you need to keep data in the Dataverse in sync with data in external systems. For example, you can use Power Automate to set up a scheduled job that imports data from an external system into the Dataverse every night.

In summary, the three major approaches for data integration into the Dataverse are data import, transform and import data using Power Query, and scheduled integration with other systems. Each approach has its own advantages and use cases, and your choice will depend on your specific data integration requirements.