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Jeffrey Njoroge
Jeffrey Njoroge

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Mastering Data Modeling in Power BI

#analytics #data #microsoft #tutorial

A good Power BI report needs a strong data model. The data model controls how fast it runs, if the math is right in the data, and how easy it is to use. This guide will teach you the basics of data modeling to help you build better reports.

Why Data Modeling Matters

Before we diving in, let me show you where poor data model leads to:

  • Slow report performance and sluggish interactions
  • Inaccurate calculations and misleading insights
  • Complex DAX formulas that are hard to maintain
  • Frustrated business users who lose trust in the data

Meet the Two Key Players of data model

1.📊 Fact Tables("What Happened")

This is your EVENT LOG. It records things that happen.
Think of it like a grocery receipt: It records actions (sales) and numbers (quantity, price).

| Date       | Product      | Quantity | Price |
|------------|--------------|----------|-------|
| Jan 10     | Apples       | 5        | $10   |
| Jan 10     | Bread        | 2        | $6    |
| Jan 11     | Apples       | 3        | $6    |
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2.📐 Dimension Tables ("Who/What/When")

These are your LOOKUP TABELS. They describe things.
Think of them like product information sheets:

Product Table:
| ProductID | ProductName | Category | Color  |
|-----------|-------------|----------|--------|
| 1         | Apples      | Fruit    | Red    |
| 2         | Bread       | Bakery   | Brown  |
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Date Table:
| DateID | Date       | Day   | Month | Year |
|--------|------------|-------|-------|------|
| 101    | Jan 10     | Monday| Jan   | 2024 |
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Now that we are done with the Two Key Players let look on the other players:

🌟The Star Schema
This is Power BI's favorite way to organize data. It's called a "star" because it looks like one with a fact table at the center.

example:

           [Date Dimension]
                ↓
[Product Dimension] → [Sales Fact Table] ← [Customer Dimension]
                ↑
        [Store Dimension]
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Key idea💡: One central table (the fact table) with all your measurement numbers, surrounded by descriptive tables (dimensions) that connect directly to it.

Real Example: Coffee Shop Sales

Fact_Sales Table (The Main Counter):

| ReceiptID | DateID | ProductID | CustomerID | CupsSold | TotalPrice |
|-----------|--------|-----------|------------|----------|------------|
| 1001      | 20240115 | P101     | C205       | 2        | $8.50      |
| 1002      | 20240115 | P102     | C206       | 1        | $4.25      |
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Why Power BI Loves the star scheme🌟

  • It thinks in stars - Built to work best with this pattern
  • Faster calculations - Less "travel time" between tables
  • Easier DAX formulas - Clearer relationships mean simpler code
  • Better performance - Optimized for this structure

The Snowflake Schema❄️

In a snowflake schema, your dimension tables get further split into more tables. It's called "snowflake" because when you draw it, the connections spread out like a snowflake's branches.

example:

[Supplier Table] → [Category Table] → [Product Table] → [Sales]
                    ↓
               [Sub-Category Table]
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key idea💡:

  • Product table connects to category table
  • Category table connects to supplier table
  • Each table specializes in one type of information

Real Example: An Online Store

Dim_Product Table:
| ProductID | ProductName  | CategoryID | SupplierID |
|-----------|--------------|------------|------------|
| 101       | Blue T-Shirt | C1         | S5         |

Dim_Category Table:
| CategoryID | CategoryName |
|------------|--------------|
| C1         | Clothing     |

Dim_Supplier Table:
| SupplierID | SupplierName | CountryID |
|------------|--------------|-----------|
| S5         | TeeCo Inc    | USA1      |
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When Might You See a Snowflake?

  • When connecting to existing databases (some are already built this way)
  • When multiple fact tables share the same dimension (like having one Date table for Sales AND Inventory)
  • When dimensions are extremely large (millions of rows )

Relationships: How Your Data Tables "Talk" to Each Other

Think of relationships in Power BI as introductions between your tables - they're how your data gets to know each other and work together!

Real-Life Example: Grocery Shopping

RECEIPT (Fact table)
| ItemID | StoreID | Price |
|--------|---------|-------|
| 1      | 5       | $3.99 |

STORE LIST (Dimension table)  
| StoreID | StoreName | City     |
|---------|-----------|----------|
| 5       | FreshMart | Boston   |

ITEM LIST (Dimension table)
| ItemID | ItemName  | Category |
|--------|-----------|----------|
| 1      | Apples    | Fruit    |
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Beginner's Best Practices

  • Always use a Date table (Power BI needs this for time calculations)
  • Keep it simple - star schema is usually best
  • Name tables clearly: "Sales" not "Table1"
  • Connect with single arrows (from dimension → fact table)
  • Hide confusing columns from the report view

Don't try to build the perfect model on day one. Start with:

  • One fact table (your main event)
  • Two dimension tables (Date + one other, like Product)
  • Simple reports

That all folks!! for any question leave a comment below and follow me on github:https://github.com/JeffreyNjoroge Happy learning!!

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