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Introduction to Microsoft Power BI Dataflows

Updated: Aug 15, 2023

What is a Dataflow, exactly?


A developer or a business user may create a dataflow, which is a basic data pipeline or sequence of stages. Data from a broad range of sources may be collected into the Power BI service using this pipeline. You may think of it as a cloud-based version of Power Query. Using the Power Query tool on the Power BI Desktop, most Power BI developers are already familiar with altering data using the Power Query tool. One of the most significant drawbacks of Power Query was that you couldn't reuse your modifications in other Power BI reports. Having to copy code from one report to another to apply the same logic was a pain in the ass for me. In addition, we had to update the logic in all of the Power BI manually reports developed when a basic logic modification had to be made.


That's where dataflows come in. You can design reusable data integration pipelines for use in many Power BI reports with Power BI Dataflows. It's also possible to share your dataflows with other people in your business, and they may also utilize the dataflows. This is an excellent feature since it eliminates additional overhead when the logic is altered. If you change your dataflow, it will immediately be reflected in all subsequent dataflows. Let's establish a Power BI Dataflow now that we're familiar with the concept.


Dataflows
Image courtesy of Microsoft Docs



Dataflows Usage Scenarios


It would be best if you used dataflows in the following cases:

  • When it comes to Power BI, the most crucial benefit is that dataflows may be shared with other users in the environment. You should create Power BI dataflows if you have a lot of Power Query (M) scripts or if you have to keep copying them across Power BI datasets.

  • Dataflows in Power BI are a low-code/no-code option. To construct your data transformations, you don't have to write a single line of code. Dataflows may be produced with Power Query Online, which tens of millions of Excel and Power BI users are already acquainted with. However, you are under no obligation to modify the "M" scripts.

  • It is possible to make these dataflows accessible in a shared Power BI Workspace managed by a specialized team, producing verified entities that can be utilized throughout the company. It is possible to include verified customer records into numerous new datasets generated by people or groups on a self-service basis.

  • To process massive volumes of data, dataflows were created. Power BI dataflows don't even need the Power BI Desktop client, thanks to the Power BI portal's capacity to execute data preparation.

  • It is possible to arrange separate dataflows with their own refresh time. Additionally, while using Power BI Premium/Embedded, you may enable incremental refresh for dataflows using DateTime columns.



What are the advantages of Power BI Premium/Embedded?


  • It's possible to plan a refresh up to 48 times a day in Power BI Premium/Embedded and up to 8 times per day in Power BI Pro.

  • You may utilize Power BI dataflows to import data progressively to prepare large datasets.

  • Faster entity refreshes are the result of parallelizing transformations.

  • It is possible to update other workspaces' associated entities. Entities in one Power BI dataflow that relate to entities in another are referred to as "linked entities."

  • You may utilize calculated entities in dataflows to build a new entity from an existing one. As a rule of thumb, entities that use in-store computations for speed and scalability are called "computed entities."



Difference between Power BI Dataflows and Datasets


Despite this, a popular question about dataflows is: What is the difference between dataflows and datasets in Power BI?


I have prepared a simple table to outline the differences for easier digestion:


Power BI Dataflows

Power BI Datasets

Implementation

CDM folder

Analysis Services tabular model

Storage

CSV files

Vertipaq

Metadata

CDM - model.json

BISM

Development

Power Query Online

Power Query Editor in Power BI Desktop

Primary purpose

Data reuse

Data analysis

Reusability

Acts as a data source in multiple datasets

Shared datasets across workspaces

Scope of reuse

Entity level reuse

Dataset level reuse

Used for reporting

Not directly

Yes

Reuse outside of Power BI

Yes, through ADLS Gen 2

Yes, through XMLA

Data access methods

Import

Import & DirectQuery

Connection methods

Import

Live Connection

Row-level security (RLS)

No

Yes


 

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