Star schema concept in Power BI - Real Business Scenario

Scenario Mode

👤 Your Role: You are a business intelligence analyst at a retail company.

📋 Request: Your manager wants you to organize sales data using a star schema to improve reporting and analysis.

📊 Data: You have raw sales transactions data including sales date, product details, store location, and sales amount.

🎯 Deliverable: Create a star schema model in Power BI with one fact table and related dimension tables, then build a simple sales report using this model.

Sample Data

SalesID	SalesDate	ProductID	ProductName	Category	StoreID	StoreName	Region	SalesAmount
1	2024-01-05	101	Notebook	Stationery	10	Central Store	North	15.00
2	2024-01-06	102	Pen	Stationery	11	East Store	East	5.00
3	2024-01-07	103	Stapler	Office Supplies	10	Central Store	North	8.00
4	2024-01-08	101	Notebook	Stationery	12	West Store	West	15.00
5	2024-01-09	104	Marker	Stationery	11	East Store	East	7.00
6	2024-01-10	105	Paper Clips	Office Supplies	12	West Store	West	3.00
7	2024-01-11	102	Pen	Stationery	10	Central Store	North	5.00
8	2024-01-12	103	Stapler	Office Supplies	11	East Store	East	8.00

1

Step 1: Create a fact table named 'FactSales' with columns: SalesID, SalesDate, ProductID, StoreID, SalesAmount.

In Power BI, import the sales data and select only these columns for the fact table.

Expected Result

FactSales table with 8 rows and columns: SalesID, SalesDate, ProductID, StoreID, SalesAmount.

2

Step 2: Create a dimension table named 'DimProduct' with unique ProductID, ProductName, and Category.

Use 'Remove Duplicates' on ProductID from sales data to create DimProduct.

Expected Result

DimProduct table with 5 unique products and their categories.

3

Step 3: Create a dimension table named 'DimStore' with unique StoreID, StoreName, and Region.

Use 'Remove Duplicates' on StoreID from sales data to create DimStore.

Expected Result

DimStore table with 3 unique stores and their regions.

4

Step 4: Create a dimension table named 'DimDate' with unique SalesDate and additional columns for Year, Month, and Day.

Extract unique SalesDate from FactSales and add calculated columns: Year = YEAR(SalesDate), Month = FORMAT(SalesDate, "MMMM"), Day = DAY(SalesDate).

Expected Result

DimDate table with 8 unique dates and Year, Month, Day columns.

5

Step 5: Create relationships in Power BI model: FactSales.ProductID to DimProduct.ProductID, FactSales.StoreID to DimStore.StoreID, FactSales.SalesDate to DimDate.SalesDate.

Use Power BI 'Manage Relationships' to create one-to-many relationships from dimensions to fact table.

Expected Result

Star schema model with FactSales at center connected to DimProduct, DimStore, and DimDate.

6

Step 6: Create a measure 'Total Sales' in FactSales to sum SalesAmount.

Total Sales = SUM(FactSales[SalesAmount])

Expected Result

Measure 'Total Sales' calculates total sales amount correctly.

7

Step 7: Build a report page with a table visual showing Total Sales by Region and Category.

Add DimStore[Region] and DimProduct[Category] as rows, and 'Total Sales' as values in the table visual.

Expected Result

Table shows sales totals grouped by Region and Category.

Final Result

Star Schema Model:

       DimProduct
          |
          |
DimStore--FactSales--DimDate


Report Table:
+---------+----------------+------------+
| Region  | Category       | Total Sales|
+---------+----------------+------------+
| East    | Stationery     | 12.00      |
| East    | Office Supplies| 8.00       |
| North   | Stationery     | 20.00      |
| North   | Office Supplies| 8.00       |
| West    | Stationery     | 15.00      |
| West    | Office Supplies| 3.00       |
+---------+----------------+------------+

✓The East and North regions have the highest total sales.

✓Stationery products generate more sales than Office Supplies.

✓The star schema model simplifies analysis by separating facts and dimensions.

Bonus Challenge

Add a new dimension table 'DimCategory' with unique product categories and link it to DimProduct. Then update the report to show sales by Category and Month.

Show Hint

Create DimCategory with Category column, relate DimProduct[Category] to DimCategory[Category], and use DimDate[Month] in the report.

Practice

1. What is the main characteristic of a star schema in Power BI?

easy

A. A single table with all data combined

B. Multiple fact tables connected in a chain

C. One central fact table connected to multiple dimension tables

D. Only dimension tables without any fact table

Solution

1. Step 1: Understand star schema structure

   A star schema has one main fact table that stores measurable data.

2. Step 2: Identify dimension tables role

   Dimension tables provide descriptive attributes and connect to the fact table.

3. Final Answer:

   One central fact table connected to multiple dimension tables -> Option C

4. Quick Check:

   Star schema = One fact + many dimensions [OK]

Hint: Remember star schema looks like a star with one center [OK]

Common Mistakes:

• Thinking multiple fact tables form a star schema
• Confusing dimension tables as fact tables
• Assuming star schema has no fact table

2. Which of the following is the correct way to describe the relationship in a star schema?

easy

A. One-to-many from dimension tables to fact table

B. Many-to-many between fact and dimension tables

C. One-to-many from fact table to dimension tables

D. One-to-one between fact and dimension tables

Solution

1. Step 1: Identify relationship direction

   Dimension tables filter many rows in the fact table, so one dimension row relates to many fact rows.

2. Step 2: Confirm relationship type

   This means a one-to-many relationship from dimension to fact table.

3. Final Answer:

   One-to-many from dimension tables to fact table -> Option A

4. Quick Check:

   Dimension to fact = one-to-many [OK]

Hint: Dimension filters many facts, so one-to-many from dimension [OK]

Common Mistakes:

• Reversing the relationship direction
• Choosing many-to-many which is not typical in star schema
• Assuming one-to-one relationships

3. Given a star schema with a Sales fact table and a Product dimension table, what happens if you slice sales by product category?

medium

A. The report shows total sales without any filtering

B. Sales values are grouped by product category from the dimension table

C. Sales values are ignored because categories are not in the fact table

D. An error occurs because dimension tables cannot filter facts

Solution

1. Step 1: Understand slicing in star schema

   Slicing by product category uses the Product dimension to filter the Sales fact table.

2. Step 2: Effect on sales values

   Sales are grouped and summed by the selected product categories.

3. Final Answer:

   Sales values are grouped by product category from the dimension table -> Option B

4. Quick Check:

   Slicing by dimension groups facts [OK]

Hint: Dimensions slice facts to group data in reports [OK]

Common Mistakes:

• Thinking dimension filters do not affect fact data
• Assuming error occurs when slicing by dimension
• Believing sales values are ignored

4. You created a star schema but your report shows incorrect totals when filtering by a dimension. What is the most likely cause?

medium

A. The report uses a pie chart instead of a bar chart

B. The fact table has duplicate rows

C. The dimension table has too many columns

D. The relationship between fact and dimension tables is inactive or missing

Solution

1. Step 1: Check relationships in star schema

   Inactive or missing relationships prevent filtering from dimension to fact table.

2. Step 2: Impact on report totals

   Without active relationships, filters do not apply correctly, causing wrong totals.

3. Final Answer:

   The relationship between fact and dimension tables is inactive or missing -> Option D

4. Quick Check:

   Active relationships needed for correct filtering [OK]

Hint: Verify relationships are active to fix filtering issues [OK]

Common Mistakes:

• Blaming duplicate rows without checking relationships
• Thinking chart type affects data filtering
• Ignoring relationship status

5. You want to optimize a Power BI model with sales data. Which star schema design improves query speed and report clarity?

hard

A. One large fact table with keys to small, focused dimension tables

B. Multiple fact tables with duplicated dimension data inside each

C. One fact table with all dimension attributes merged inside it

D. Only dimension tables with no fact table

Solution

1. Step 1: Understand star schema optimization

   A large fact table with keys to small dimension tables reduces data duplication and improves performance.

2. Step 2: Avoid poor designs

   Duplicating dimension data or merging all attributes in fact table causes slow queries and confusion.

3. Final Answer:

   One large fact table with keys to small, focused dimension tables -> Option A

4. Quick Check:

   Star schema = big fact + small dimensions [OK]

Hint: Keep fact big and dimensions small for best speed [OK]

Common Mistakes:

• Duplicating dimension data in fact tables
• Merging all data into one table
• Removing the fact table entirely