Data Modeling with Microsoft Power BI (Final Release)КНИГИ » ОС И БД
Название: Data Modeling with Microsoft Power BI: Self-Service and Enterprise Data Warehouse with Power BI (Final Release) Автор: Markus Ehrenmueller-Jensen Издательство: O’Reilly Media, Inc. Год: 2024 Страниц: 526 Язык: английский Формат: epub Размер: 11.9 MB
Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher.
Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems.
This book is your companion on your journey to gain a comprehensive understanding about the steps needed to make building reports in Power BI Desktop and Power BI Report Builder, and creating measures in DAX, easier. Power BI supports a wide variety of data sources (covering databases from different vendors, like Microsoft, Oracle or Teradata; flat files, like CSV, text, or Excel; web services like an https link to a web page, etc.). The only way to get data into Power BI is through Power Query. It’s best practice to add calculations as (explicit) measures in DAX (as opposed to calculated columns in DAX or as columns in Power Query or in the data source). Creating calculated tables in DAX should be an exception; depending on your skills and preferences, you will implement transformations to shape the data model either in Power Query (in the user interface or by writing code in the M language) or in the data source.
The first part of this book will introduce you to the necessary concepts in a general way, which is written in an agnostic way: You can apply this to any analytical system. In the second part you will learn about the properties of a data model in Power BI. The rest of this book works its way over DAX, Power Query to SQL. The book is designed for you, the reader, to have an individual experience, based on your knowledge. You may not know DAX, Power Query and SQL. But it is up to your choice, to pick and choose to fill in gaps in your knowledge. Maybe you need a refresher on which parts a data model consists of? Part I has you covered. Maybe you struggle with a bunch of Excel files on which you need to create reports? The part on Power Query will be your starting point. Maybe your task is to build a data warehouse to which other people connect with Power BI Desktop? Then the part about SQL will present you with solutions to typical problems.
The book is designed for you, the reader, to have an individual experience based on your knowledge. You may not know DAX, Power Query, and SQL, but you may have familiarity with one or two of them; you can pick and choose to fill in gaps in your knowledge. Maybe you need a refresher on the composition of a data model. Part I has you covered. Maybe you struggle with dealing with a bunch of Excel files from which you need to create reports? The part on Power Query will be your starting point. Maybe your task is to build a data warehouse to which other people connect with Power BI Desktop? Then the part about SQL will present you with solutions to typical problems.
This book shows you how to:
Normalize and denormalize with DAX, Power Query, and T-SQL Apply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensions Solve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQL Discover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQL Work with tables, relations, set operations, normal forms, dimensional modeling, and ETL
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Data Modeling with Microsoft Power BI (Final Release) 
