Power BI Dataflow

 

📌 Session Outline

1️⃣ What is a Power BI Dataflow? (3 min)

• Power BI Dataflows are cloud-based ETL (Extract, Transform, Load) solutions for self-service data preparation.
• Built on Power Query and stored in Azure Data Lake for reuse across reports.
• Helps in creating centralized, reusable datasets for multiple reports and users.

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2️⃣ Why Use Power BI Dataflows? (3 min)

✅ Data Reusability – Once created, multiple reports can use the same cleaned dataset.
✅ Performance Optimization – Reduces data refresh load on reports.
✅ Automated Data Processing – Helps in scheduled refresh and incremental data loading.
✅ Supports Large Data Volumes – Works efficiently with big datasets.

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3️⃣ Steps to Create a Power BI Dataflow (10 min - Demo)

📌 Step 1: Open Power BI Service

• Go to Power BI Online (app.powerbi.com).
• Navigate to Workspaces → Click on Create → Dataflow.

📌 Step 2: Define Dataflow Entities

• Choose "Add Tables" → Connect to a Data Source (e.g., SQL Server, SharePoint, Excel, API).
• Apply Transformations in Power Query (clean, filter, merge, split).

📌 Step 3: Save & Refresh the Dataflow

• Click Save & Close → Name your Dataflow.
• Set up Scheduled Refresh to automate updates.

📌 Step 4: Use Dataflow in Power BI Desktop

• Open Power BI Desktop → Get Data → Power BI Dataflows.
• Select the required table(s) and load them into the model.

📌 Step 5: Build Reports Using Dataflow Data

• Use the dataflow tables just like any other dataset to create reports and dashboards.

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4️⃣ Best Practices for Power BI Dataflows (3 min)

✔ Use Incremental Refresh for large datasets to reduce load time.
✔ Store in Azure Data Lake for better scalability.
✔ Leverage Power Query best practices for optimized performance.
✔ Document Dataflows for easy team collaboration and maintenance.

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🔹 Summary & Q&A (1 min)

• Power BI Dataflows help in centralized, reusable, and automated data preparation.
• They improve performance, consistency, and efficiency in Power BI reporting.