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11. Data Modelling

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Come on, friends, now the foundation of the report – Data Modelling. With a good model DAX becomes easy; with a bad model you fight for every measure. In this module we properly understand fact and dimension tables (a table of descriptive details), the star schema, relationships and the Date table.

What you will learn in this module

  • Fact tables vs dimension tables
  • Star schema vs snowflake schema
  • Relationships: cardinality, cross-filter direction, active and inactive
  • Building a date table with CALENDAR / CALENDARAUTO and marking it as a date table
  • Modelling best practices

Live story: Why the city slicer did nothing

Suppose we load Orders and DarkStore tables for the Blinkit Maharashtra report. We add a City slicer. Pune, Nashik and Mumbai all show the same ₹1,500. Nothing is wrong with the numbers. The tables are simply not connected. This chapter builds a star schema: fact and dimension tables, relationships, and a Date table for the April–March financial year.

All numbers in this story are fictional practice data.

Concepts in this chapter

  1. 11.1Why Data Modelling Matters
  2. 11.2Fact Tables and Dimension Tables
  3. 11.3Star Schema vs Snowflake Schema
  4. 11.4Relationships
  5. 11.5The Date Table
  6. 11.6Hierarchies and Groups
  7. 11.7Model View Properties
  8. 11.8Modelling Best Practices
  9. 11.9A Flat Table versus a Dimensional Model
  10. 11.10Role-Playing Dimensions
  11. 11.11Bridge Tables and Many-to-Many
  12. 11.12Perspectives

Review and practice

  1. ★Practice and Answer Key: Data Modelling (Star Schema)

The chapter recap is at the end of the last concept page.

Ravi Sir's Cards: 16 revision, interview and comparison cards for this chapter · मराठी cards