https://github.com/sarathchandranpm/data-modelling-creating-a-star-schema
A data modeling project that transforms raw sales and budget data into an optimized dimensional model in Power BI. The implementation includes star schema design with multiple dimension tables for sales analysis, enhanced with budget tracking capabilities through a bridge table solution for category-segment relationships.
https://github.com/sarathchandranpm/data-modelling-creating-a-star-schema
data-modeling normalization powerbi
Last synced: 3 months ago
JSON representation
A data modeling project that transforms raw sales and budget data into an optimized dimensional model in Power BI. The implementation includes star schema design with multiple dimension tables for sales analysis, enhanced with budget tracking capabilities through a bridge table solution for category-segment relationships.
- Host: GitHub
- URL: https://github.com/sarathchandranpm/data-modelling-creating-a-star-schema
- Owner: SarathchandranPM
- Created: 2024-12-21T15:09:17.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-22T09:18:25.000Z (7 months ago)
- Last Synced: 2025-02-15T16:49:46.695Z (5 months ago)
- Topics: data-modeling, normalization, powerbi
- Homepage:
- Size: 44.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Sales Data Model Project in Power BI - Part 1
## Overview
This part of the project demonstrates the implementation of a dimensional data model (primarily star schema with snowflake elements) in Power BI using a sales dataset. The model transforms a flat CSV file into an optimized dimensional structure, improving data analysis capabilities and reporting performance.
## Data Source
The original dataset was provided as an Excel CSV file containing sales transactions with the following fields:
* ProductID, Date, CustomerID, CampaignID, Units, Product, Category, Segment
* ManufacturerID, Manufacturer, Unit Cost, Unit Price
* Customer details: ZipCode, Email, Name
* Geographic information: City, State, Region, District, Country
## Data Model Architecture
### Fact Table
The central fact table **Sales** contains the core transaction metrics and foreign keys:
* Measures: Units, Unit Cost, Unit Price
* Foreign Keys: ProductID, CustomerID, Date
* Additional Fields: Date, Manufacturer
### Dimension Tables
The model includes four dimension tables:
#### 1. Dim Products
Contains product-related attributes:
* ProductID (Primary Key)
* Product
* Category
* Segment
#### 2. Dim Customers
Stores customer information:
* CustomerID (Primary Key)
* Email
* First Name
* Last Name
* ZipCode (Foreign Key to Dim Geography)
#### 3. Dim Geography
Contains location hierarchies:
* ZipCode (Primary Key)
* City
* State
* Region
* District
* Country
*Note: This dimension introduces a snowflake element as it connects to the fact table through Dim Customers rather than directly.*
#### 4. Dim Date
A dedicated date dimension marked as the official date table:
* Date
* Day of Week
* Month
* Month Number
* Quarter
* Week Number
* Year
## Model Characteristics
### Star Schema Implementation
The model primarily follows a star schema design with these key characteristics:
* Centralized fact table (Sales)
* Denormalized dimension tables
* Single join path between fact and most dimensions
* Simplified query paths for better performance
### Snowflake Element
The geographic dimension (Dim Geography) introduces a snowflake element by:
* Connecting to the fact table through Dim Customers
* Using ZipCode as the linking field between Dim Customers and Dim Geography
## Development Process
The project followed a structured approach to data modeling:
1. **Conceptual Modeling**
* Identified core business entities
* Defined relationships between entities
* Determined fact and dimension candidates
2. **Logical Modeling**
* Designed the star schema structure
* Defined primary and foreign keys
* Established table relationships
3. **Physical Modeling**
* Implemented the model in Power BI
* Created appropriate data types
* Established relationships with correct cardinality
* Optimized for performance
## Learning Outcomes
This project provided valuable insights into:
* Star schema design principles and implementation
* Data normalization importance and techniques
* Dimensional modeling best practices
* Power BI data modeling capabilities
* The balance between performance and model complexity
# Sales and Budget Data Model Enhancement - Part 2
## Overview:
This phase of the project extends the existing sales data model by incorporating budget data, demonstrating advanced data modeling techniques to handle many-to-many relationships and complex dimensional hierarchies.
## New Data Structure
### New Fact Table
**FactBudget** was introduced with the following fields:
* Value (measure)
* Date (foreign key)
* Scenario
* Original Category and Segment fields (later replaced with CatSeg Key)
## Data Modeling Challenges and Solutions
### Handling Many-to-Many Relationships
* Identified a potential many-to-many relationship between FactBudget and product dimension tables through Category
* Implemented a bridge table solution to resolve the many-to-many relationship
* Created a new dimension table (Dim CatSeg) to establish proper relationships
### Creation of Bridge Dimension
**Dim CatSeg** was created through the following steps:
* Extracted Category and Segment combinations from Dim Category
* Removed duplicates to ensure distinct Category-Segment pairs
* Created a surrogate key (CatSeg Key) using the index column technique
* Established as the central dimension for category-related attributes
### Data Transformation Process
1. **Merge Operations**
* Merged Dim CatSeg with FactBudget to incorporate CatSeg Key
* Performed similar merge operation with Dim Product
* Based merge operations on Category and Segment columns
2. **Data Cleanup**
* Removed redundant Category and Segment columns from FactBudget
* Cleaned up Dim Product by removing duplicate category information
* Maintained data integrity while reducing model complexity
## Model Optimization
### Relationship Management
* Established proper relationships between:
* FactBudget and Dim Date through Date column
* FactBudget and Dim CatSeg through CatSeg Key
* Dim Product and Dim CatSeg through CatSeg Key
## Data Model Architecture
**Final Data Model:**
**Sales table and related dimension tables:**
**FactBudget table and related dimension tables:**
### Performance Considerations
* Implemented surrogate keys for efficient joins
* Removed redundant columns to reduce model size
* Optimized relationship cardinality for better query performance
## Technical Skills Demonstrated
### Advanced Data Modeling
* Resolution of many-to-many relationships
* Creation and implementation of bridge tables
* Surrogate key generation and management
### Data Transformation
* Complex merge operations
* Duplicate removal and data deduplication
* Column management and cleanup
### Dimensional Modeling
* Bridge dimension design and implementation
* Proper key management
* Relationship optimization
### Power BI Features Utilized
* Merge queries functionality
* Index column creation
* Relationship management
* Data model optimization techniques
## Business Intelligence Best Practices
* Maintained dimensional modeling standards
* Implemented proper key management
* Ensured data integrity across the model
* Created efficient paths for data analysis
## Results
The enhanced data model now supports:
* Integrated analysis of sales and budget data
* Proper handling of category-segment hierarchies
* Efficient querying across multiple fact tables
* Streamlined reporting capabilities
This phase of the project demonstrates advanced data modeling techniques and showcases the ability to handle complex business requirements while maintaining model efficiency and analytical capabilities.
## Tools Used
* Power BI Desktop
* Excel (source data)