Power BI Dataflows

Power BI Dataflows are an integral part of the Power BI ecosystem, streamlining data preparation and offering enhanced scalability compared to traditional datasets. With the right expertise, they can be set up swiftly, providing more control over data pipelines.?

Understanding Power BI Dataflows

Power BI Dataflows serve as a self-service data preparation layer within the Power BI environment. They enable users to ingest, transform, and load data from various sources, offering a clean and structured foundation for building insightful reports and dashboards. In many ways, Dataflows function like centralized datasets, featuring many of the same options and interfaces.

Dataflows vs Datasets

While datasets can perform many of the same functions as dataflows, dataflows offer unique advantages by storing transformations further upstream. This centralization allows multiple datasets to draw from a single source without replicating logic across different datasets. Consequently, reports rely on a single data thread through the dataflow, rather than hitting a source multiple times, which simplifies the creation of new reports that need to pull from a primary source within an organization.

When creating a new report, you only need to pull the necessary tables that already have the applied transformations, reducing overhead both in tidying up datasets and managing the data.

Getting Started

1. Accessing Power BI Dataflows

• To begin using Dataflows, navigate to the Power BI service.
• Open the Power BI workspace where you want to create the Dataflow.
• Select the "New" dropdown and choose Dataflows.

2. Creating a Blank Dataflow

• The setup process depends on your needs. For a fresh start, choose to define new tables. Alternatively, you can connect to other workspaces or import from existing dataflows.

3. Setting up the data source

• Dataflows support the same connection types as datasets, making it easy to connect to existing data sources. If a gateway connection is available, it will automatically populate in the "Connection" field after proper configuration.

4. Transforming Data

• Dataflows support Power Query transformations similar to those in datasets, making it easy to apply your existing knowledge to this tool. Once you've added a table from a source, you can add more sources or tables by using the "Get Data," "Duplicate," or "Enter Data" options. After completing your transformations, click "Save & Close."

5. Dataflow created:

After creating your dataflow, you can implement options like:

• Incremental Refresh: Refresh only new data within a specified period to reduce refresh times and resource usage.
• Scheduled Refresh: Similar to datasets, you can configure specific times for dataflow refreshes. It is recommended to trigger these refreshes before dataset refreshes to ensure that newer data is available.

Setup

One of Power BI's standout features is the ability to easily copy a transformed table from a Power BI dataset into a dataflow. Since dataflows use the same Power Query as datasets, you can copy and paste tables from open report files within the Power Query editor into dataflows. All applied steps will automatically carry over, and if connected to the original source, you will see the same data. This feature simplifies setting up dataflows based on existing datasets, allowing for quick and easy usage.

Dataset

Dataflow

Limitations of Dataflows

While dataflows are powerful, they do have some limitations. One notable issue is the restricted visibility with ownership—only dataflow owners can directly view tables and their associated options. This limitation can be frustrating for those in a workspace with multiple users, as it can be difficult to understand the contents of a dataflow without direct ownership. Even with full permissions, the visibility of dataflows is limited compared to datasets or reports within workspaces. A possible workaround is to export the dataflow for better visibility.

Conclusion

Power BI Dataflows have become a key component in the data preparation process, offering organizations a scalable, efficient, and collaborative approach to data management. By leveraging the capabilities of Dataflows, organizations can gain greater control over their data while making it easier to start working with them.