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Understanding Power BI Dataflow:

29 June 2023

Understanding Power BI Dataflow: An Essential Guide

Delving into the world of data analysis, we encounter a plethora of tools and techniques to leverage. Among these, Power BI stands out as a comprehensive business analytics tool that transforms big data into insightful visualisations. A critical component in Power BI is Power BI Dataflow, a cloud-based data shaping and preparation service.

So, what does Power BI Dataflow do and why is it crucial in data analysis? This article will answer these questions and more as we navigate through the labyrinth of Power BI Dataflow.

Understanding Power BI Dataflow


What is Power BI Dataflow?

In essence, Power BI Dataflow is a self-service extract, transform, load (ETL) tool. It enables you to cleanse, transform and unify data from various disparate sources into a common schema. This processed data, now available for consumption by multiple users, is stored in a cloud-based storage service, commonly known as Azure Data Lake Storage Gen2.

Now, you might wonder, "How does Power BI Dataflow work?"

The Working Mechanism of Power BI Dataflow

Power BI Dataflow utilises ETL processes to organise data into a more readable format, ready for analysis. It begins with the extraction of data from a diverse range of sources, followed by the transformation of this data by cleaning, enriching, and restructuring it, and finally, loading the transformed data into a common destination.

The key benefit of using Power BI Dataflow? It enables business analysts and other users to develop and manage data preparation processes with minimal assistance from IT or data professionals, thus democratizing data analytics and encouraging more proactive decision-making processes within organisations.

Components of Power BI Dataflow

Now, let's deconstruct Power BI Dataflow into its primary components: ETL processes, entities, and mapping Dataflows to Power BI datasets.

ETL Processes in Power BI Dataflow

Power BI Dataflow operates through a sequence of ETL processes. But what is ETL? It stands for Extract, Transform, and Load. These three core processes form the backbone of data integration strategies, including Power BI Dataflow.

Entities in Power BI Dataflow

In Power BI Dataflow, an 'entity' refers to a set of fields used to store data, similar to a table in a database. Entities enable analysts to structure data in a more coherent and accessible way, streamlining subsequent analysis and reporting tasks.

Mapping Dataflows to Power BI Datasets

Once data is refined and stored as entities, Power BI Dataflow allows for the mapping of these dataflows to Power BI datasets. This capability enhances the flexibility and efficiency of data analytics by reducing redundancy and ensuring consistency across multiple reports and dashboards.

Creating and Using Power BI Dataflow

The creation and utilisation of Power BI Dataflow may seem intricate at first. Let's break it down into manageable steps.

Prerequisites for Creating a Power BI Dataflow

Before you create a Power BI Dataflow, ensure you have a Power BI account and necessary access permissions. You should also have a clear understanding of the data sources and transformations you intend to use.

Step-by-step Guide to Creating a Power BI Dataflow

Creating a Power BI Dataflow involves a series of steps, starting from opening the Power BI service to defining transformations, mapping entities, and finally, refreshing the dataflow. This process, although detailed, can be mastered with practice and familiarity.

Understanding Power BI Dataflow's Interface

Being familiar with the Power BI Dataflow interface is vital for efficient dataflow management. From the home page to the query editor and the dataflow settings, every element plays a crucial role in your data analysis journey.

Loading Data into the Dataflow

Loading data into the data

flow is a critical part of the ETL process. Depending on your data source, you may need to install a gateway and set up connection details. Once done, you can start importing data into your dataflow.

This brings us to an important question: "How can we make the most of Power BI Dataflow?" This is where best practices come into play.

Best Practices for Power BI Dataflow

To ensure efficient and accurate data analysis, consider the following best practices for Power BI Dataflow:

  1. Design and Structure Dataflows Thoughtfully: A well-designed dataflow makes data management easier and more effective. It's best to structure your dataflows based on your specific data analysis needs and objectives.

  2. Schedule Refreshes Responsibly: Regularly refreshing your dataflow ensures your reports and dashboards are always up-to-date. However, be mindful of your refresh schedule's impact on system resources.

  3. Manage and Optimise Dataflows Regularly: Regular dataflow management and optimisation can significantly improve performance. It's advisable to review your dataflows periodically, removing unnecessary entities and refining transformation processes.

Power BI Dataflow vs Power BI Dataset

Understanding the distinction between Power BI Dataflow and Power BI Dataset can significantly impact your data analysis approach. Both have unique features and are suited to different scenarios.

The main difference lies in their primary functions. While Power BI Dataflow focuses on the ETL process and storage of data, Power BI Dataset concerns data modelling and reporting. Deciding between them often depends on your particular use case and the complexity of the data you are working with.

Case Study: Real-world Application of Power BI Dataflow

Having explored the theoretical aspects of Power BI Dataflow, let's see how it operates in a real-world scenario. Consider a large retail company that generates data from multiple sources, such as sales records, customer reviews, and social media feeds.

Before the implementation of Power BI Dataflow, the company struggled to manage and analyse this wealth of information. However, with Power BI Dataflow, they were able to unify their data into one accessible platform. This resulted in more informed decision-making, leading to increased sales and improved customer satisfaction.


Power BI Dataflow represents a significant stride in the democratization of data analysis, simplifying the complex ETL process into an accessible and efficient tool. By understanding and leveraging its capabilities, organisations can gain deeper insights into their data, driving more informed decision-making and yielding competitive advantage. You can also create visualisations using the Power BI visuals marketplace.

FAQs about Power BI Dataflow

Q1: What is Power BI Dataflow? A1: Power BI Dataflow is a cloud-based data preparation tool that extracts, transforms, and loads data from various sources into a common schema.

Q2: How does Power BI Dataflow work? A2: Power BI Dataflow works by executing ETL processes that extract data from multiple sources, transforms it into a readable format, and then loads it into a common destination.

Q3: What is the difference between Power BI Dataflow and Power BI Dataset? A3: Power BI Dataflow focuses on the ETL process and data storage, while Power BI Dataset is about data modelling and reporting.

About the author: Craig Hartzel
Craig is a self-confessed geek who loves to play with and write about technology. Craig's especially interested in systems relating to e-commerce, automation, AI and Analytics.

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