Analyzing Data with Power BI and Power Pivot for Excel
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Sprache:Englisch
32,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
19.04.2017
Verlag
Pearson Education LimitedSeitenzahl
256
Maße (L/B/H)
22,9/18,7/1,5 cm
Gewicht
424 g
Auflage
1
Sprache
Englisch
ISBN
978-1-5093-0276-5
Renowned DAX experts Alberto Ferrari and Marco Russo teach you how to design data models for maximum efficiency and effectiveness.
How can you use Excel and Power BI to gain real insights into your information? As you examine your data, how do you write a formula that provides the numbers you need? The answers to both of these questions lie with the data model. This book introduces the basic techniques for shaping data models in Excel and Power BI. It’s meant for readers who are new to data modeling as well as for experienced data modelers looking for tips from the experts. If you want to use Power BI or Excel to analyze data, the many real-world examples in this book will help you look at your reports in a different way—like experienced data modelers do. As you’ll soon see, with the right data model, the correct answer is always a simple one!
By reading this book, you will:
• Gain an understanding of the basics of data modeling, including tables, relationships, and keys
• Familiarize yourself with star schemas, snowflakes, and common modeling techniques
• Learn the importance of granularity
• Discover how to use multiple fact tables, like sales and purchases, in a complex data model
• Manage calendar-related calculations by using date tables
• Track historical attributes, like previous addresses of customers or manager assignments
• Use snapshots to compute quantity on hand
• Work with multiple currencies in the most efficient way
• Analyze events that have durations, including overlapping durations
• Learn what data model you need to answer your specific business questions
About This Book
• For Excel and Power BI users who want to exploit the full power of their favorite tools
• For BI professionals seeking new ideas for modeling data
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