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

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Acquisitions Editor: Devon Musgrave

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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