- •Contents at a glance
- •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
- •Code Snippets
compute the amount invoiced for the order selection is a slight variation from the previous one, as you can see in the following code:
Click here to view code image
Amount Invoiced := CALCULATE (
SUM ( OrdersInvoices[Amount] ),
CROSSFILTER ( OrdersInvoices[Invoice], Invoices[
)
You sum the Amount column in the bridge table and the CROSSFILTER function activates bidirectional filtering between the bridge table and Invoices. The result of using this formula is much more interesting because you can now easily spot the amount ordered and invoiced for every order, and you can obtain reports like the one shown in Figure 3-21.
FIGURE 3-21 Using the bridge table, you can produce a report showing the amount ordered and invoiced.
Conclusions
In this chapter, you learned how to handle different scenarios with multiple fact tables that are, in turn, related through dimensions or through bridge tables. The most important topics you learned in this chapter are as follows:
If you denormalize too much, you reach a point at which your tables are over-denormalized. In such a scenario, filtering different fact tables becomes impossible. To correct this, you must build a proper set of dimensions to be able to slice values from the different fact tables.
Although you can leverage DAX to handle over-denormalized scenarios, the DAX code quickly becomes too complex to handle. A change in the data model makes the code much easier.
Complex relationships between dimensions and fact tables can create ambiguous models, which cannot be handled by the DAX engine. Ambiguous models must be solved at the data-model level by duplicating some tables and/or denormalizing their columns.
Complex models like orders and invoices involve multiple fact tables. To model them the right way, you must build a bridge table so that the information is related to the correct entity.