In this section, we will explore the concept of dataflows in Power BI, how to create them, and how to manage them effectively. Dataflows are a powerful feature in Power BI that allow you to extract, transform, and load (ETL) data from various sources into a centralized location for further analysis and reporting.

Dataflows are a self-service data preparation tool in Power BI that allows you to:

• Extract data from various sources.
• Transform data using Power Query.
• Load data into the Power BI service for use in reports and dashboards.

Dataflows are particularly useful for:

• Centralizing data preparation tasks.
• Reusing data preparation logic across multiple reports and datasets.
• Ensuring data consistency and quality.

1. Navigate to Power BI Service:

   • Open your web browser and go to the Power BI service (https://app.powerbi.com).

2. Select a Workspace:

   • Choose the workspace where you want to create the dataflow. You can create a new workspace if needed.

3. Create a New Dataflow:

   • Click on the "Create" button and select "Dataflow" from the dropdown menu.

4. Define Entities:

   • Click on "Add new entities" to start defining the data you want to include in your dataflow.
   • Choose a data source (e.g., SQL Server, Excel, SharePoint, etc.).
   • Provide the necessary connection details and credentials.

5. Transform Data:

   • Use Power Query to transform the data. You can perform various transformations such as filtering rows, renaming columns, merging tables, etc.
   • Click "Next" once you have completed the transformations.

6. Save and Load:

   • Name your dataflow and save it.
   • The dataflow will start loading the data into the Power BI service.

Here is an example of creating a dataflow that extracts data from an Excel file, transforms it, and loads it into the Power BI service.

1. Open the Dataflow:

   • Navigate to the workspace where the dataflow is located.
   • Click on the dataflow you want to edit.

2. Edit Entities:

   • Click on "Edit entities" to modify the existing entities or add new ones.
   • Make the necessary changes using Power Query.

3. Save Changes:

   • Save the dataflow to apply the changes.

1. Manual Refresh:

   • Navigate to the dataflow and click on the "Refresh" button to manually refresh the data.

2. Scheduled Refresh:

   • Set up a scheduled refresh to automatically refresh the dataflow at specified intervals.
   • Go to the dataflow settings and configure the refresh schedule.

1. View Refresh History:

   • Check the refresh history to monitor the success or failure of dataflow refreshes.
   • Navigate to the dataflow settings and click on "Refresh history".

2. Error Handling:

   • Review any errors that occurred during the refresh process and take corrective actions.
   • Use the error messages to identify and fix issues in the dataflow.

Objective: Create a dataflow that extracts data from a CSV file, transforms it, and loads it into the Power BI service. Then, set up a scheduled refresh and monitor the dataflow.

Steps:

1. Create a Dataflow:

   • Navigate to Power BI Service and select a workspace.
   • Click on "Create" and select "Dataflow".
   • Add a new entity from a CSV file.
   • Perform transformations such as filtering rows and renaming columns.
   • Save the dataflow.

2. Set Up Scheduled Refresh:

   • Go to the dataflow settings and configure a daily refresh schedule.

3. Monitor the Dataflow:

   • Check the refresh history to ensure the dataflow is refreshing successfully.
   • Review any errors and fix them if necessary.

Solution:

In this section, we have learned about creating and managing dataflows in Power BI. Dataflows are a powerful tool for centralizing data preparation tasks and ensuring data consistency. We covered the steps to create a dataflow, perform data transformations, set up scheduled refreshes, and monitor the dataflow. By mastering dataflows, you can streamline your data preparation process and improve the efficiency of your Power BI reports and dashboards.