This project presents an interactive Power BI Sales Dashboard built using a combination of Excel-generated data and Google BigQuery as the data source. It demonstrates a full data analytics workflow including:
- Data generation (synthetic dataset)
- Data storage (BigQuery)
- Data transformation (Power Query)
- Data modeling (relationships + schema)
- Data visualization (Power BI dashboards)
The goal is to deliver business insights through clean, interactive, and user-friendly reports.
- Yared Zenebe
- Yeabsira Fikadu
- Yeabsira Mekonnen
- Yeabsira Gebremichael
- Yeabsira Zelalem
- Yetnayet Lakew
The project uses three main tables:
Contains transactional sales data:
- Order details (Order ID, Dates, Ship Mode)
- Customer info (Name, Segment, Region)
- Product details (Category, Sub-category)
- Metrics (Sales, Profit, Quantity, Discount)
Maps regional managers:
- Person
- Region
Tracks returned orders:
- Returned (Yes/No)
- Order ID
- Region
Synthetic data is generated using a custom Python script:
- ~5000 rows of realistic sales data
- Randomized customers, regions, and products
- Simulated return rate (~15%)
CSV Files → Google BigQuery → Power BI → Dashboard
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Data loaded from BigQuery tables:
orderspeoplereturns
Performed in Power Query:
-
Data type corrections
-
Created calculated columns:
Delivery DaysYear
-
Cleaned and structured dataset
- Loaded into Power BI for modeling and visualization
-
Star/Snowflake schema design
-
Relationships:
- Orders ↔ Returns (Order ID)
- Orders ↔ People (Region)
- Total Revenue
- Total Orders
- Average Delivery Days
- Return Rate
- Profit Analysis
- Yearly Trends
- KPI Cards
- Line Charts
- Bar & Column Charts
- Donut Charts
- Map Visualization
- Scatter Plot
- Slicers
- Drill-through
- Tooltips
- Clear filter button
- Page navigation
- Power BI Desktop
- Google BigQuery
- Python (Pandas)
- Excel / CSV
- Power Query
- DAX
- Generate CSV data
- Upload to BigQuery
- Connect Power BI
- Transform data
- Build dashboard