What is Dataflow in Power BI?

If you're using Power BI, you might have encountered a feature called "dataflows" and wondered what they are and why you should care. In short, Power BI dataflows are a complete game-changer for anyone who’s tired of repeating the same data cleaning steps over and over again. This article breaks down exactly what dataflows are, how they work, and why they might just become your new favorite Power BI feature.

What is a Power BI Dataflow?

Think of a dataflow as a reusable recipe for your data. You perform all the preparation work - connecting to sources, cleaning messy columns, transforming data - just once, and save that "recipe" in the Power BI cloud service. From then on, you and your team can simply use that perfectly prepared data in as many different Power BI reports as you want, without ever having to repeat the prep work.

At its core, a Power BI dataflow is a cloud-based, self-service data preparation process. It uses Power Query Online, the web version of the same powerful data transformation tool you use in Power BI Desktop, to extract, transform, and then load data into a storage location in the cloud (specifically, Azure Data Lake Storage Gen2, managed by Microsoft).

The result is a collection of clean, ready-to-use tables, called entities, that can be easily connected to as a source in Power BI Desktop to build reports and dashboards.

Why Should You Use Power BI Dataflows? The Key Benefits

On the surface, it might seem like an extra step, but incorporating dataflows into your workflow offers some massive advantages, especially as your reporting needs grow.

1. Create Reusable Data Preparation Logic

This is the number one reason to use dataflows. Imagine your company gets monthly sales data from a partner as a messy, poorly formatted CSV file. For every new sales report you create, you have to go through the same tedious 20-step process in Power Query: remove the top 5 rows, split columns, unpivot data, filter out junk values, and rename everything.

With a dataflow, you do that once. You build a dataflow that connects to the source, performs all 20 cleaning steps, and produces a beautiful, clean table. Now, for any future report, you simply connect to that dataflow and get the clean table instantly. You've saved yourself hours and ensured everyone is using the exact same logic.

2. Centralize Your Business Logic

When data transformations are buried inside dozens of individual Power BI Desktop files (.pbix files), keeping things consistent is a nightmare. What happens when a business rule changes? For example, if the definition of an "Active Customer" changes, you’d have to find every single report that uses this logic, open it, and manually update the Power Query steps.

Dataflows solve this by centralizing that logic in the cloud. You update the "Active Customer" definition in one place - the dataflow - and every single report connected to it automatically inherits the change after the next refresh. This drastically simplifies maintenance and reduces the risk of inconsistent reporting.

3. Empower Collaboration and Consistency

Dataflows make it radically easier for teams to work together. Instead of everyone creating their own versions of common datasets like "Customers," "Products," or "Sales Transactions," you can have a single, official dataflow that serves as the "single source of truth." Your sales team, marketing department, and finance analysts can all connect to the same pre-vetted, clean data, ensuring everyone's reports are aligned and consistent.

4. Improve Report Performance and Efficiency

Dataflows separate the heavy-lifting of data transformation (the 'T' in ETL) from the data modeling and visualization parts of your report. The dataflow runs on its own schedule in the Power BI service, refreshing the data independently of your reports. When your Power BI report refreshes, it's just pulling in the already-processed data, which can be much faster than having the report's desktop engine re-run complex transformations from the original raw sources every time.

Dataflow vs. Dataset vs. Power Query: Clearing Up the Confusion

These terms are often used interchangeably, but they represent distinct components in the Power BI ecosystem. Understanding the difference is crucial.

• Power Query: This is the engine or tool you use to connect to and transform data. It's the editor with all the buttons for removing columns, filtering rows, and merging queries. You use Power Query inside Power BI Desktop, Excel, and as the engine behind dataflows in Power BI Service (Power Query Online).
• Dataflow: This is a cloud-based ETL process and its output. It uses the Power Query engine to run your transformation steps and stores the resulting clean tables in the cloud. It is a reusable source of prepared data.
• Dataset (Semantic Model): This is what powers your report visuals. A dataset is what you're creating inside your .pbix file. It contains one or more data sources (which can include dataflows!), the data model (relationships between tables), calculated columns, and DAX measures. It's the final, report-ready layer.

A Simple Cooking Analogy

To make it even clearer, let’s think about cooking a meal:

• Power Query is your kitchen setup: your knives, cutting boards, mixing bowls, and cooking skills.
• A Dataflow is like preparing meal kits. You go to the grocery store (your raw data sources), then wash, chop, and marinate all your ingredients (transforming the data). You then package these perfectly prepared kits (clean tables) and store them in the fridge (Azure Data Lake).
• A Dataset is the final dish you assemble. You take your prepared meal kits (the dataflow tables), maybe add some extra sources like a jar of spices (another data source), create relationships (combining ingredients), and add your special DAX sauce (measures).
• The Power BI Report is how you present the meal on the plate (your charts and graphs).

How to Create a Power BI Dataflow: A Step-by-Step Guide

Creating your first dataflow is surprisingly straightforward because it uses an interface you're already familiar with. Dataflows are created in the Power BI service, not the desktop app.

Step 1: Navigate to Your Power BI Workspace

Log in to app.powerbi.com and go into the workspace where you want to create your dataflow. It’s best practice to use a dedicated workspace for dataflows.

Step 2: Create a New Dataflow

In your workspace, select the + New button and then choose Dataflow from the list.

Step 3: Define New Tables

You'll see a few options. Since this is your first time, you'll want to add new tables from a source. Click on Add new tables. (Note: "Link tables" and "Import model" are more advanced options that let you reuse and build upon existing dataflows or datasets.)

Step 4: Choose Your Data Source

Now you'll see a familiar screen showing hundreds of data connectors, just like in Power BI Desktop. For this example, let's pull a simple table from a web page. Search for the "Web Page" connector, enter a URL (e.g., the URL of a Wikipedia page with a table), and click Next.

Step 5: Select Data and Transform It in Power Query Online

After connecting, Power BI will show you the available tables from that source in the Navigator. Select the table(s) you need and click Transform data. You are now in the Power Query Online editor! It looks and feels almost identical to the editor in Power BI Desktop. You can now perform all your usual cleaning and transformation steps: change data types, remove or rename columns, filter rows, etc. Once your data is clean and shaped the way you want, click Save & close.

Step 6: Name Your Dataflow and Configure Refresh Settings

Power BI will prompt you to name your dataflow. Give it a descriptive name (e.g., "Weekly Company Metrics"). After saving, find your dataflow in the workspace list, click the three-dot menu (...) and go to Settings. Here, you can set up a Scheduled refresh. This is critical. You can configure your dataflow to refresh daily or weekly, ensuring the data is always up-to-date, completely independent of any reports.

How to Use Your Dataflow in a Report

Once your dataflow is created and refreshing, using it is the easiest part.

1. Open a new or existing Power BI Desktop file.
2. Go to the Home ribbon, click Get Data, and search for Power BI dataflows.
3. Click Connect. A navigator will appear, allowing you to browse through your Power BI workspaces to find your dataflow.
4. Open your dataflow, and you’ll see the clean tables (entities) you created. Select the ones you want to use and click Load.

That's it! The perfectly prepared data is now in your Power BI report, ready for you to build a data model and create stunning visualizations. No in-report Power Query steps needed.

Final Thoughts

Power BI dataflows fundamentally change your analytics workflow for the better. By separating data prep logic from reports, you create a reusable, maintainable, and collaborative system that saves countless hours and ensures everyone in your organization is working from a single source of truth.

Centralizing and preparing data is a critical step, no matter which tools you use. While dataflows are brilliant for users inside the Power BI ecosystem, the real challenge often lies in just getting all your scattered data together in the first place - especially from platforms like Google Analytics, Shopify, Facebook Ads, and Salesforce. That's a huge focus for us at Graphed . We simplify that initial connection and dashboarding process by doing the heavy lifting for you, letting you ask questions in plain English to build real-time reports instantly, helping you get to insights faster without a steep learning curve.