Describe dataflows for Microsoft Dataverse

Dataflows in Microsoft Dataverse

Dataflows in Microsoft Dataverse, formerly known as Common Data Service (CDS), provide a powerful way to bring data from various sources, transform it, and load it into Dataverse tables. Dataflows automate the process of data integration and preparation, making it easier to aggregate, transform, and unify data for analysis and reporting. In this article, we will explore the concept of dataflows and how they are used in Microsoft Dataverse.

Introduction to Dataflows

Dataflows in Dataverse are similar to Extract, Transform, Load (ETL) processes in traditional data integration scenarios. They enable users to connect to different data sources, perform data transformation operations, and load the transformed data into Dataverse tables.

With dataflows, users can extract data from various sources such as databases, cloud services, spreadsheets, or even online sources like web pages or REST APIs. They can apply data transformation operations like filtering, sorting, merging, or aggregating the data to meet specific requirements. The transformed data is then loaded into one or more Dataverse tables, making it easily accessible for reporting, analysis, or other business applications.

Creating Dataflows

Dataflows are created and managed using Power Query, a powerful data transformation tool available within Microsoft Dataverse. Power Query provides a visual interface for designing dataflows, making it accessible to users with little or no coding experience.

Using Power Query, users define the data sources, establish connections, and specify the desired transformations on the data. They can apply various transformation steps, including filtering, sorting, merging, aggregating, or even creating calculated columns. Power Query generates a logical representation of the applied transformations, which is then executed when the dataflow is refreshed.

Refresh and Schedule

Dataflows need to be periodically refreshed to maintain the accuracy and freshness of the data. The refresh process loads the latest data from the connected data sources, applies the defined transformations, and updates the Dataverse tables with the refreshed data.

Users can manually refresh dataflows at any time to immediately bring in the latest data. Additionally, they can schedule automatic refreshes on a predefined frequency, ensuring that the data is kept up to date without manual intervention.

Reuse and Sharing

Dataflows can be reused and shared across multiple applications and environments within Microsoft Dataverse. Users can create a dataflow once and then leverage it in different projects or business applications. This reusability reduces redundant efforts and ensures consistency in data integration and preparation.

Dataflows can also be shared with other users or teams, allowing them to utilize the same dataflow in their own applications. This promotes collaboration and efficiency in managing data integration processes.

Integration with Power BI

Dataflows in Dataverse seamlessly integrate with Power BI, enabling users to leverage the transformed data directly in Power BI reports and dashboards. Power BI can connect to the dataflows within the same Dataverse environment and leverage the unified, transformed data for analysis and visualization.

By combining dataflows and Power BI, users can build comprehensive data analytics solutions, ensuring that accurate and up-to-date data is available for reporting and decision-making.

In conclusion, dataflows provide a flexible and efficient way to integrate, transform, and load data into Microsoft Dataverse. With dataflows, users can automate the process of data preparation, making it easier to aggregate and unify diverse data sources. By leveraging dataflows, organizations can ensure data accuracy, consistency, and accessibility for analytics, reporting, and business applications.