Fabric Project

Scenario:
An online shopping company wants to analyze:
    • Sales performance
    • Customer behavior
    • Top-selling products
Goal: Turn raw data into insights for business decisions

Step-by-Step E-Commerce Data Flow:
This is a real-world data pipeline built using Microsoft Fabric and visualized using Power BI.

Step 1: Data Ingestion (Pipeline)
In the first step, the company collects data from multiple sources such as the website orders database, payment system, and product catalog files like CSV. All this data is automatically brought into the system using pipelines, which ensures daily and continuous data loading without manual effort.

Step 2: Store in OneLake
After ingestion, all raw data is stored in OneLake. This includes orders data, customer details, and product information. OneLake acts as a central storage system where all organizational data is kept in one place for easy access and management.

Step 3: Data Transformation (Notebook)
Next, the data is cleaned and processed using notebooks with Python or Spark. In this step, duplicate orders are removed, missing values are handled, date formats are corrected, and new calculations like total sales (price × quantity) are created. This step ensures the data is accurate and ready for analysis.

Step 4: Load into Fabric Warehouse
After cleaning, the processed data is loaded into the Fabric Warehouse in a structured format. It is organized into tables such as Orders Table, Customers Table, and Products Table. This structure allows fast querying and efficient data analysis.

Step 5: Visualization in Power BI
Finally, the structured data is used in Power BI to create dashboards and reports. Dashboards show total sales, monthly revenue trends, and top products, while reports provide insights into customer purchasing patterns and region-wise sales performance. This helps the company make better business decisions using data insights.

Final Understanding
This complete flow shows how raw e-commerce data is transformed into meaningful business insights using Microsoft Fabric and visualized through Power BI.

Orders/Customers/Products → Pipeline → OneLake → Notebook (Clean & Transform) → Warehouse → Power BI Dashboard

Summary: e-commerce project:

Pipeline → Collects raw business data
OneLake → Stores all data centrally
Notebook → Cleans and transforms data
Warehouse → Organizes structured data
Power BI → Generates insights