Power BI dataflows are a data preparation feature that operates in the Power BI. They use Power Query Online to connect to source systems, run transformation steps, and store the structured output in Azure Data Lake Storage. A dataflow consists of one or more query entities, each representing a table created from applied transformations.
It functions as a cloud-based layer where data is ingested, shaped, and stored using standard Power Query M logic.
If you’re trying to get a better picture of what this component actually does or how it fits inside your overall Power BI environment, you’re in the right place. Because in this article, we’ll walk through what Power BI dataflows are, how they differ from datasets, and where they usually sit in a modern reporting setup.
Let’s begin.
What Are Power BI Dataflows?
Power BI dataflows are a cloud-based feature inside the Power BI Service that prepares and shapes data before it moves into a dataset.
They use Power Query Online to connect to different data sources, apply transformation steps, and create a set of structured tables. These tables are stored in Azure Data Lake Storage or Dataverse, depending on how your workspace is set up.
A dataflow works as the layer where your data is ingested and transformed using the same Power Query logic you use in Power BI Desktop, but the process happens in the browser instead of a PBIX file.
Each dataflow contains one or more tables, and each table is the result of the transformation steps you define in Power Query Online.
Dataflows do not act as a full data model. They do not hold relationships, measures, or DAX expressions. They simply store the shaped output from your transformation steps.
Now we understand what Power BI dataflows are. But, what’s the difference between dataflows and datasets?
Power BI dataflows handle the preparation stage, while datasets handle the modelling and analysis stage.
Here is a simple way to see the difference.
| Aspect | Dataflows | Datasets |
|---|---|---|
| Aspect | Dataflows | Datasets |
| Purpose | Extract, clean, and transform data | Model data and build calculations |
| Storage | Azure Data Lake Storage or Dataverse | In-memory engine in Power BI |
| Editing | Power Query Online | Power BI Desktop or Service |
| Structure | Tables only, no relationships | Tables with relationships, measures, and DAX |
| Use | Shared transformation logic for many reports | Direct reporting and visualisation |
| Reusability | Tables reused across multiple datasets | DAX reused across multiple reports |
| Data Lineage | Connects to the original source | Can connect to dataflows or direct sources |
| Common Users | Data engineers and analysts | Report developers and business users |
The main difference is that a dataflow prepares the data, while a dataset models the data. They sit in different layers of Power BI, but they work together as part of the same data pipeline.
Benefits of Using Power BI Dataflows
Power BI dataflows sit at the very start of your reporting process, so the way they handle your data has a big impact on everything that comes after.
Because they prepare and organise your data in one shared place, they help your team work with cleaner tables, smoother refreshes, and a more predictable structure across your reports.
Before diving into the details, here are the main benefits you’ll notice when using dataflows.
- One place to prepare your data: Dataflows give you a central spot to clean and shape your data. You set your Power Query steps once, and every report can use the same cleaned tables.
- Faster refresh with incremental refresh: Dataflows can refresh only the data that has changed instead of reloading everything. So if you have years of historical data, you only update the recent part.
- A cleaner split between preparation and modelling: Dataflows handle the preparation work, while datasets handle the modelling. This separation keeps your reports lighter and helps Power BI push some of the work back to the source through query folding.
- Better scale without heavy cost: Because your data is processed once at the dataflow level, you don’t repeat the same work in dozens of reports. This can lower processing costs and makes it easier to support several datasets that rely on the same source.
- Built-in data profiling tools: Power Query Online shows you things like missing values, duplicates, or odd patterns in your data. You can spot issues early before the data reaches your reports.
- Easier collaboration across your team: Different people can work on different tables or steps at the same time. This makes it easier for teams to build and update dataflows together without blocking each other.
- Smooth integration with the Microsoft ecosystem: Dataflows integrated well with Azure Data Lake Storage, Azure Data Factory, and other Microsoft services. This gives you a path to more advanced processing when you need it.
- Clear data lineage: Dataflows show you where your data comes from and how it changes along the way. This helps with transparency, documentation, and audits when you need to explain your data path.
These benefits make dataflows an important part of a clean and organised Power BI setup. Now that you’ve seen what they bring to the table, let’s look at how you can use Power BI dataflows in your own workflow.
How to Use Power BI Dataflows
Here’s a simple step-by-step guide to help you create and use Power BI dataflows. This follows the actual flow in the Power BI Service, written in warm, easy-to-follow language while keeping the technical terms intact.
Before you start, there are a few things you need in place before you can create a dataflow:
- A Power BI Pro, PPU, or Premium licence: Dataflows aren’t available on the free tier.
- A shared workspace: You can’t create dataflows in My Workspace. They must sit in a shared workspace that’s on Premium or PPU.
- Dataflows enabled in the Admin Portal: If you’re using Premium capacity, an admin needs to turn them on under Tenant Settings.
Once these are set, you can start building your dataflow. Here’s the step-by-step process:
Step 1: Open your workspace
Go to the Power BI Service and open the Premium workspace where you want the dataflow to live. Select New to begin.
Step 2: Choose “Dataflow”
From the dropdown, pick Dataflow. This opens the creation options.
Step 3: Pick your creation method
You’ll see four ways to build a dataflow:
- New Source (connect to a new data source)
- Linked Tables (use tables from another dataflow)
- CDM Folder (use a Common Data Model folder)
- Import or Export (move existing dataflows in or out)
For most setups, you’ll choose New Source.
Step 4: Connect to your data source
Select the source you want to use, such as Excel, SQL Server, SharePoint, a database, or an API. Add your credentials, confirm the connection details, and choose the tables you want to bring in.
Step 5: Transform your data
Power Query Online will open. This is where you clean and shape your data. You can filter rows, rename columns, merge tables, split fields, or add formulas. Every change you make becomes part of your transformation steps.
Step 6: Save and apply your dataflow
Select Close & Apply. Power BI will ask you to name your dataflow and add an optional description. Once you save it, the dataflow appears in your workspace.
Step 7: Set up scheduled refresh
Open the dataflow settings and turn on Scheduled refresh. Choose how often it should refresh, the time zone, and the exact times. If you’re on Premium, you can also set up incremental refresh so the dataflow processes only new or recent data.
Step 8: Use your dataflow in Power BI Desktop
Open Power BI Desktop, go to Get Data, and choose Dataflows. You can import the tables into your model or use DirectQuery if the Enhanced Compute Engine is enabled.
When to Use Power BI Dataflows?
Power BI dataflows are helpful when your data starts to become too much for a single dataset or PBIX file to manage. They give you one shared place in the cloud to clean and prepare your data before it reaches your reports. This makes things easier when your setup starts getting bigger or more complex.
Here are the some situations where a dataflow makes sense:
- You need the same transformed tables across several reports : If different reports rely on the same Power Query steps, a dataflow lets you build those steps once and reuse them everywhere. This avoids version mismatches and keeps every report aligned.
- Your source system is slow or heavily used: When your data comes from a slow on-prem database or a source that gets queried often, a dataflow acts as a central cache. Reports pull from the dataflow instead of hitting the original source repeatedly.
- You work with large time-based data that grows every day: Dataflows support incremental refresh, which makes them a strong fit for logs, transactions, finance data, or anything that expands over time. Only the new or changed rows are processed.
- You need to combine or enrich data from several systems: If your workflow involves merging tables, creating aggregates, or joining data from different sources, a dataflow gives you a clean staging layer to organise that work in one place.
- Your team has multiple developers building reports at once: Dataflows help teams work in parallel. The preparation logic lives in one shared layer, so developers don’t have to recreate the same steps in separate PBIX files.
- Your reports put heavy load on your source system: When many reports pull from the same on-prem database, routing those pulls through a dataflow can noticeably reduce the pressure on that system.
These situations make it easier to decide whether your setup needs a dataflow or if a regular dataset is enough for what you’re building.
Best Practices for Managing Power BI Dataflows
When you start using dataflows regularly, a few habits can make your setup smoother and much easier to look after. You don’t need anything fancy, just some simple practices that keep your dataflows clean, organised, and predictable as they grow.
Here are the five that matter most.
Use clear and consistent naming
Good naming makes a huge difference. Give your dataflows, tables (entities), and Power Query steps names that describe what they actually are.
For example, use Sales_Transactions_2024 instead of Table1. Clear names help your team understand the structure at a glance and make it easier to find the right table when several dataflows start building up.
Keep your transformation steps simple
Dataflows run in the cloud, so simple, clean transformations refresh faster and cause fewer issues. Try to avoid long chains of complex steps when a single merge, filter, or clean-up would do. The goal is to make each table easy to read, easy to refresh, and easy to adjust later.
Split heavy logic into smaller dataflows
When a single dataflow handles too many tasks, it becomes slow, hard to refresh, and difficult for others to follow. If your logic keeps growing, break it into smaller dataflows and use linked tables to bring them together. This creates a cleaner pipeline and makes troubleshooting much easier.
Remove data you don’t need
Extra columns and unnecessary rows slow everything down. Before saving your dataflow, check what you actually need for your reports. Dropping unused fields or trimming old records helps your refresh run faster and keeps your datasets lighter.
Set up incremental refresh for growing tables
If your data grows over time (like logs, transactions, or daily files) turn on incremental refresh. This lets the dataflow process only the new or updated rows instead of reprocessing the entire history. It keeps your refresh times stable as your data volume increases.
These simple practices give you a solid base for managing dataflows without extra complexity.
How Nexalab Can Help
Working with Power BI dataflows can feel a bit overwhelming when your data comes from many places or when your team needs a setup that is clean, consistent, and easy to maintain.
This is exactly where having the right support makes a real difference.
Nexalab helps you build a Power BI environment that’s organised from the starts.
As part of our marketing analytics consulting, we help you bring all your marketing data into one clear structure, fix inconsistencies, set up reliable tracking, and make sure your dataflows feed clean tables into your reports. This gives your team a stable foundation instead of juggling messy or mismatched data from different tools.
We also work as a specialised Power BI consultant in Australia.
If you’re building a reporting setup around dataflows, we can design your data model, set up your refresh logic, define your transformation steps, and build dashboards that show the metrics that matter. Whether you need simple marketing reports or a full multi-layered Power BI solution, we help you put the pieces together in a way that makes sense.
With the right structure in place, your dataflows become easier to manage, your datasets stay consistent, and your reporting environment grows without turning complicated.
A Few Takeaways Before You Go
Power BI dataflows give you a clean way to prepare and organise your data before it reaches your datasets. They sit in the preparation layer, helping you bring data in, shape it with Power Query Online, and store it in a structured form.
They’re most useful when several reports need the same transformed tables, when your source systems are slow or complex, or when your data grows over time and needs features like incremental refresh. With a few good practices (like clear naming, simple transformations, and removing unused data) your dataflows stay easier to manage as your reporting grows.
And if you ever feel that setting up or maintaining dataflows on your own is a bit too complicated, you don’t have to struggle through it. Nexalab can help you build a setup that’s clear, stable, and ready to grow with your needs.
Book a free consultation to get your Power BI environment running smoothly.
