Analyzing Data with Power BI and Power Pivot for Excel (Business Skills) PDF
Preview Analyzing Data with Power BI and Power Pivot for Excel (Business Skills)
Analyzing Data with Microsoft Power BI and Power Pivot for Excel Alberto Ferrari and Marco Russo PUBLISHED BY Microsoft Press A division of Microsoft Corporation One Microsoft Way Redmond, Washington 98052-6399 Copyright © 2017 by Alberto Ferrari and Marco Russo. All rights reserved. No part of the contents of this book may be reproduced or transmitted in any form or by any means without the written permission of the publisher. Library of Congress Control Number: 2016931116 ISBN: 978-1-5093-0276-5 Printed and bound in the United States of America. First Printing Microsoft Press books are available through booksellers and distributors worldwide. If you need support related to this book, email Microsoft Press Support at [email protected]. Please tell us what you think of this book at https://aka.ms/tellpress. This book is provided “as-is” and expresses the author’s views and opinions. The views, opinions and information expressed in this book, including URL and other Internet website references, may change without notice. Some examples depicted herein are provided for illustration only and are fictitious. No real association or connection is intended or should be inferred. Microsoft and the trademarks listed at https://www.microsoft.com on the “Trademarks” webpage are trademarks of the Microsoft group of companies. All other marks are property of their respective owners. Acquisitions Editor: Devon Musgrave Editorial Production: Polymath Publishing Technical Reviewer: Ed Price Copy Editor: Kate Shoup Layout Services: Shawn Morningstar Indexing Services: Kelly Talbot Editing Services Proofreading Services: Corina Lebegioara Cover: Twist Creative • Seattle Contents at a glance Introduction Chapter 1 Introduction to data modeling Chapter 2 Using header/detail tables Chapter 3 Using multiple fact tables Chapter 4 Working with date and time Chapter 5 Tracking historical attributes Chapter 6 Using snapshots Chapter 7 Analyzing date and time intervals Chapter 8 Many-to-many relationships Chapter 9 Working with different granularity Chapter 10 Segmentation data models Chapter 11 Working with multiple currencies Appendix A Data modeling 101 Index Contents Introduction Who this book is for Assumptions about you Organization of this book Conventions About the companion content Acknowledgments Errata and book support We want to hear from you Stay in touch Chapter 1. Introduction to data modeling Working with a single table Introducing the data model Introducing star schemas Understanding the importance of naming objects Conclusions Chapter 2. Using header/detail tables Introducing header/detail Aggregating values from the header Flattening header/detail Conclusions Chapter 3. Using multiple fact tables Using denormalized fact tables Filtering across dimensions Understanding model ambiguity Using orders and invoices Calculating the total invoiced for the customer Calculating the number of invoices that include the given order of the given customer Calculating the amount of the order, if invoiced Conclusions Chapter 4. Working with date and time Creating a date dimension Understanding automatic time dimensions Automatic time grouping in Excel Automatic time grouping in Power BI Desktop Using multiple date dimensions Handling date and time Time-intelligence calculations Handling fiscal calendars Computing with working days Working days in a single country or region Working with multiple countries or regions Handling special periods of the year Using non-overlapping periods Periods relative to today Using overlapping periods Working with weekly calendars Conclusions Chapter 5. Tracking historical attributes Introducing slowly changing dimensions Using slowly changing dimensions Loading slowly changing dimensions Fixing granularity in the dimension Fixing granularity in the fact table Rapidly changing dimensions Choosing the right modeling technique Conclusions Chapter 6. Using snapshots Using data that you cannot aggregate over time Aggregating snapshots Understanding derived snapshots Understanding the transition matrix Conclusions Chapter 7. Analyzing date and time intervals Introduction to temporal data Aggregating with simple intervals Intervals crossing dates Modeling working shifts and time shifting Analyzing active events Mixing different durations Conclusions Chapter 8. Many-to-many relationships Introducing many-to-many relationships Understanding the bidirectional pattern Understanding non-additivity Cascading many-to-many Temporal many-to-many Reallocating factors and percentages Materializing many-to-many Using the fact tables as a bridge Performance considerations Conclusions Chapter 9. Working with different granularity Introduction to granularity Relationships at different granularity Analyzing budget data Using DAX code to move filters Filtering through relationships Hiding values at the wrong granularity Allocating values at a higher granularity Conclusions Chapter 10. Segmentation data models Computing multiple-column relationships Computing static segmentation Using dynamic segmentation Understanding the power of calculated columns: ABC analysis Conclusions Chapter 11. Working with multiple currencies Understanding different scenarios Multiple source currencies, single reporting currency Single source currency, multiple reporting currencies Multiple source currencies, multiple reporting currencies Conclusions Appendix A. Data modeling 101 Tables Data types Relationships Filtering and cross-filtering Different types of models Star schema Snowflake schema Models with bridge tables Measures and additivity Additive measures Non-additive measures Semi-additive measures Index
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