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Dataflow Power BI: everything you need to know about the self-service ETL tool

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dataflow power bi

Dataflow Power BI: Power BI's Dataflow is a self-service data integration tool for Microsoft's Business Intelligence platform. Find out how Dataflow works and how to learn how to use it.

In general, a Dataflow Power BI is an ETL (extraction, transformation, loading) pipeline that can be used to connect data sources, transform data by applying specific rules, and prepare data for visualization.

In a traditional data architecture, a dedicated ETL tool is used to prepare and transform data before loading it into the Data Warehouse. However, the Microsoft Power BI business intelligence solution was used to connect to the Data Warehouse and visualize data directly.

However, developing such a data integration pipeline requires a significant amount of time. It involves considering numerous design elements and adopting best practices.

This is not optimal because a company evolves so rapidly that it can be challenging for IT teams to keep up. To address this issue, Microsoft developed a comprehensive and fully managed data preparation tool for Power BI.

This tool can be used not only by professional developers but also by business users. It allows users to connect to data sources and prepare data for reporting and visualization.

According to Microsoft, dataflows are not a replacement for a Data Warehouse architecture or ETL procedures but an addition to an analytical environment.

What is a Dataflow?

A Dataflow as in Dataflow Power BI is a straightforward term that refers to a data pipeline or a sequence of steps that can be created by either a developer or a business user. This pipeline serves the purpose of transferring data to Power BI from a wide array of sources.

Think of a Dataflow as akin to Power Query for the Cloud. As a result, developers already adept at data transformation through Power Query in the desktop version of Power BI will swiftly grasp this tool.

The same data preparation functionalities, data source connectors, gateways, and transformations are present. Dataflows are crafted and managed within the online Power BI environment, alongside datasets, dashboards, and reports, all housed within a Power BI workspace.

What are the advantages of Dataflow Power BI?

Power BI Dataflows offer several advantages. One of the key strengths is their reusability.

A significant limitation of Power Query was the inability to reuse data transformations for other Power BI reports. It often required copying existing code from one report to another.

With Power BI Dataflows, this concern has been addressed. Now, it’s possible to effortlessly create data integration pipelines that can be freely reused across multiple Power BI reports. Additionally, dataflows can be shared with other users within the organization.

These users can then freely leverage these dataflows, even within a shared Power BI Workspace. Another noteworthy aspect is its low-code / no-code nature. There’s no need to write a single line of code to create data transformations. Dataflows are generated using the powerful Power Query Online tool, a tool already familiar to the tens of millions of Excel and Power BI users. Advanced users can also review or modify the “M” scripts.

Furthermore, dataflows are designed to handle large volumes of data. You don’t even need a Power BI desktop client to create a dataflow, as data transformation can be performed on the Power BI portal itself.

Lastly, individual dataflows can be scheduled based on their unique refresh needs. With Power BI Premium / Embedded features, it’s possible to enable incremental refresh for dataflow entities containing a DateTime column.

What is the difference between Dataflow and Dataset?

In tandem with Dataflows, Datasets are another integral component of Power BI. A Dataset serves as the object containing the connection to the data source, data tables, the data itself, table relationships, and DAX calculations.

These two components exhibit several distinctions. While Dataflows replace Power Query, Datasets take over the realm of DAX calculations and relationships. Once created, both Dataflows and Datasets can be reused through the utilization of a shared dataset.

In terms of their roles, Dataflows act as the data transformation layer of Power BI, enabling the ETL (extraction, transformation, loading) process for data. On the other hand, Datasets handle the modeling and calculation layer. Data from Dataflows or other sources is captured to create an in-memory model using the Power BI analysis engine.

Regarding interaction, Dataflows transmit the outcomes of data processing to Datasets. The latter then crafts visualizations based on these data results.

Another distinction lies in how Dataflows directly access data sources, whereas Datasets access data from Dataflows.

The skills required to wield these two components differ. Dataflow developers must be adept at Power Query. On the other hand, a Dataset developer needs comprehensive knowledge of Power BI relationships and DAX calculations. While they might also be proficient in Power Query and visualization, it’s not their primary expertise.

How do I create Dataflows in Power BI?

Power BI Dataflows: It’s important to note that Dataflows are only available in the Pro and Premium versions of Power BI. The standard version doesn’t offer this option.

To create a dataflow, click on “New” within the workspace and select “Dataflow” from the menu. You can then proceed to create a dataflow in four different ways.

The first method involves defining new entities. This is the best choice if you’re starting a dataflow from scratch, importing data into the Power BI model.

The second approach is to link entities from other dataflows. This is used when connecting a new dataflow to an existing one to leverage the logic that’s already been implemented. The existing dataflow is read-only and cannot be edited.

The third method is importing a model. This entails selecting a reference dataflow and then freely modifying the defined logic. A new dataflow is created, containing the logic from the original along with any added logic. The original dataflow remains unchanged.

Lastly, the fourth approach is attaching a common data model folder. This involves choosing an existing common data model folder that has been created by another dataflow. A common data model folder structure is created by Power BI Dataflows on Azure Data Lake Gen2.

You now have a comprehensive understanding of Power BI Dataflows and the methods for creating ETL pipelines in Power BI. In summary, Dataflows are a self-service data integration tool directly available in Power BI.

It allows you to aggregate data from different sources and create a cloud-based data model based on dataset schemas. The key advantage is the ability to reuse dataflows within the organization, thus creating modular ETL pipelines to prepare datasets.

How do I learn to use Power BI?

The Dataflow is just one of the many tools within Microsoft Power BI. To master this platform and its numerous features, you can opt for DataScientest’s training programs.

This platform lies at the core of our “Business Intelligence” module in our Data Analyst track. This module also covers the Tableau tool and the concept of Data Modeling.

The other modules in the program encompass programming, Data Visualization, text data extraction and management, and Big Data. Upon completing this training, you’ll possess all the necessary skills to excel in the role of a Data Analyst.

This profession involves analyzing data and presenting it in the form of visualizations or reports. This enables executives and managers in companies to make better decisions based on data insights. In high demand across all industries, this expertise opens doors to abundant job opportunities and competitive salaries.

DataScientest’s training programs stand out with an innovative Blended Learning approach, combining both in-person and remote learning. The curriculum primarily unfolds on a cloud-based online platform, supplemented by mandatory Masterclasses.

The BootCamp mode enables completing the training at an intensive pace in just a few weeks. Alternatively, the Continuous Training option allows you to balance the program with ongoing work commitments.

Among our alumni, 85% have secured employment immediately after training. Our programs, crafted by professionals, empower learners to address the real needs of businesses. Explore the Data Analyst training and enroll now!


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