Star schema concept in Power BI - Dashboard Guide

Dashboard Mode - Star schema concept

Business Question

How can we organize sales data to easily analyze total sales by product, region, and time?

Sample Data

Sales Fact Table

SaleID	ProductID	RegionID	DateID	SalesAmount
1	101	201	301	100
2	102	202	302	200
3	101	201	302	150
4	103	203	303	300
5	102	202	301	250

Product Dimension Table

ProductID	ProductName	Category
101	Pen	Stationery
102	Notebook	Stationery
103	Chair	Furniture

Region Dimension Table

RegionID	RegionName
201	East
202	West
203	North

Date Dimension Table

DateID	Date	Month	Year
301	2024-01-01	January	2024
302	2024-02-01	February	2024
303	2024-03-01	March	2024

Dashboard Components

• KPI Card: Total Sales
  Formula: Total Sales = SUM(Sales[SalesAmount])
  Result: 100 + 200 + 150 + 300 + 250 = 1000
• Bar Chart: Sales by Product
  Formula: SUM(Sales[SalesAmount]) grouped by Product[ProductName]
  Results:
  • Pen: 100 + 150 = 250
  • Notebook: 200 + 250 = 450
  • Chair: 300
• Pie Chart: Sales by Region
  Formula: SUM(Sales[SalesAmount]) grouped by Region[RegionName]
  Results:
  • East: 100 + 150 = 250
  • West: 200 + 250 = 450
  • North: 300
• Table: Sales by Month
  Formula: SUM(Sales[SalesAmount]) grouped by Date[Month]
  Results:
  • January: 100 + 250 = 350
  • February: 200 + 150 = 350
  • March: 300

Dashboard Layout

+----------------+---------------------+
|   Total Sales  |   Sales by Product   |
|    (KPI)       |     (Bar Chart)      |
+----------------+---------------------+
|  Sales by Region (Pie Chart)           |
+---------------------------------------+
|          Sales by Month (Table)       |
+---------------------------------------+

Interactivity

Filters on Product Category, Region Name, and Year allow users to select specific categories, regions, or years. When a filter is applied, all components update to show sales data only for the selected items. For example, selecting Category = 'Stationery' updates the KPI, bar chart, pie chart, and table to show sales only for Pens and Notebooks.

Self Check

If you add a filter for Region = West, which components update and what sales amounts do they show?

• All components update.
• Total Sales: 200 + 250 = 450
• Sales by Product: Notebook = 450
• Sales by Region: West = 450
• Sales by Month: January = 250, February = 200

Key Result

A dashboard showing total sales and sales breakdown by product, region, and month using a star schema data model.

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