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Architecting Power BI Solutions in Microsoft Fabric

Architecting Power BI Solutions in Microsoft Fabric

By : Nagaraj Venkatesan
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Architecting Power BI Solutions in Microsoft Fabric

Architecting Power BI Solutions in Microsoft Fabric

5 (1)
By: Nagaraj Venkatesan

Overview of this book

Business Intelligence (BI) tools like Power BI are used by a wide range of professionals, creating diverse and complex scenarios, and finding the right solution can be daunting, especially when multiple approaches exist for a single use case. The author distills his 17 years of experience on various data platform technologies in this book to walk you through various Power BI usage scenarios. The book is structured around Power BI usage scenarios, such as developing solutions for corporate BI reporting, self-service BI reporting, and Power BI for data scientists and independent software vendors (ISVs). Each part highlights common data issues encountered in the usage scenario, the correct approach to solve the problems, and supporting technical guidance. The chapters also introduce you to some of the latest enhancements in Power BI, such as Microsoft Fabric integration with Power BI, AI features like Copilot, Power BI Git integration, and Power BI Governance features. By the end of this book, you’ll have learned how to design optimal solutions using Power BI components and pick the right tool for the job, while adhering to security and performance best practices. *Email sign-up and proof of purchase required
Table of Contents (26 chapters)
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1
Part 1: Power BI Fundamentals
6
Part 2: Designing Enterprise BI Solutions
14
Part 3: Power BI for Business Users
17
Part 4: Power BI for Data Scientists
19
Part 5: Power BI for Administrators

Introducing dataflows

As introduced in Chapter 1, dataflows let you build data transformation operations inside Power BI. They allow us to build an intermediate data store that contains the transformed data so that it can be consumed across reports. Dataflows store the transformed data in JSON format, which resides in an Azure Data Lake managed by Power BI. You can also bring in your Azure Data Lake account, attach it to a workspace, and let the dataflow store the transformed data there so that it can be managed by you. Doing this makes the transformed data available so that it can be used by other services, such as Azure Databricks, Azure Machine Learning, and others.

In the next section, we’ll create a dataflow and learn about the various configuration options we must consider.

Creating a dataflow

Let’s go through the steps to create a dataflow:

  1. To create a new dataflow, go to your Power BI workspace and click New | Dataflow, as shown in Figure 6.1...
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