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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

Deciding on an
Intermediate Data Store

After deciding on a storage mode, something we did in the previous chapter, one of the challenges data modelers face is deciding on intermediate storage. But why do we need intermediate storage? Let’s say you have a set of 25 tables that are being used across multiple reports. Consider the following requirements:

  • Each report doesn’t need all 25 tables and perhaps requires a subset of them (say 10 to 15 tables)
  • Each report would like to perform additional data processing (add a new calculated column, filter a few rows, and so on) on the 25 common tables
  • Each report might need a few other smaller tables from other data sources that may not belong to the 25 common tables

To address these requirements, each report could pull the 25 tables from the data source(s) and prepare a semantic model. However, that’s a waste of resources since each report would have to copy the same bunch of tables into their semantic...

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