From 68eaee2381326a7764e10754148feedd21ee58d5 Mon Sep 17 00:00:00 2001
From: Emmanuel Cheruiyot
 <114336972+emmanuel-cheruiyot737@users.noreply.github.com>
Date: Mon, 25 Aug 2025 08:26:38 -0500
Subject: [PATCH] Update README.md

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-# azure-data-engineer---multi-source
\ No newline at end of file
+# azure-data-engineer---multi-source
+
+## Project Overview
+
+This project implements an **end-to-end retail data pipeline** using Azure Data Engineering tools. The pipeline ingests data from multiple sources, transforms it into a structured data lake format (Bronze → Silver → Gold layers), and enables business reporting through Power BI.
+
+---
+
+## Tech Stack:
+
+- Azure Data Factory (ADF) → Data ingestion
+
+- Azure Data Lake Storage (ADLS) → Central data lake (Bronze/Silver/Gold)
+
+- Azure Databricks → Data cleaning, transformation, aggregation
+
+- Power BI → Reporting & dashboards
+
+---
+
+## 📂 Data Sources
+
+**1.** **Azure SQL Database**
+
+-**Transactions**(sales data)
+
+- **Stores** (store details)
+
+- **Products** (catalog details)
+
+**2.** API / JSON
+
+- Customers (customer master data in JSON format)
+
+---
+
+## 🏗️ Architecture
+```
+graph LR
+
+A[Azure SQL - Transactions] --> B[ADF]
+C[Azure SQL - Stores] --> B
+D[Azure SQL - Products] --> B
+E[API - Customers JSON] --> B
+B --> F[ADLS - Bronze]
+F --> G[Databricks - Silver Layer]
+G --> H[Databricks - Gold Layer]
+H --> I[Power BI Reports]
+```
+---
+
+- **Bronze Layer** → Raw ingestion from SQL & JSON
+
+- **Silver Layer** → Cleaned, standardized, joined dataset
+
+- **Gold Layer** → Aggregated business-level KPIs for reporting.
+
+---
+
+## ⚙️ Data Processing
+**Bronze Layer**
+
+- Raw parquet files from ADF ingestion
+
+- Stores, Products, Transactions, Customers as-is
+
+**Silver Layer**
+
+- Cleaned using PySpark (```retail projects - multiple tables (1).py```):
+
+- Schema alignment (casting datatypes)
+
+- Removing duplicates
+
+- Joining customers, stores, products, transactions
+
+- Adding derived column: ```total_amount = quantity * price```
+
+**Gold Layer**
+
+- Aggregated metrics created:
+
+- ```total_quantity_sold```
+
+- ```total_sales_amount```
+
+- ```number_of_transactions```
+
+- ```average_transaction_value```
+
+Stored as Delta tables for optimized analytics.
+
+## 📊 Business Requirements & KPIs
+**1.** Total Sales by Store and Category
+
+- Metric: SUM(total_sales_amount)
+
+- Dimensions: Store, Category
+
+- Visualization: Grouped Bar Chart
+
+**2.** Daily Sales Trend by Product
+
+- Metric: SUM(total_sales_amount)
+
+- Dimensions: Date, Product
+
+- Visualization: Line Chart (time series)
+
+**3.** Average Order Value per Store
+
+- Metric: ```SUM(total_sales_amount) / COUNT(transaction_id)```
+
+- Visualization: Column Chart (per store)
+
+**4.** Heatmap: Store vs Sales
+
+- **Metric:** SUM(total_sales_amount)
+
+- **Dimensions:** Store × Product/Category
+
+- **Visualization:** Heatmap (intensity by sales)
+
+---
+
+## 📑 Power BI Reporting
+
+- Connect Power BI to Gold Layer Delta tables
+
+- Create dashboard with:
+
+   - 📊 Sales by Store & Category
+
+  - 📈 Daily Sales Trend by Product
+
+   - 💰 AOV per Store
+
+   - 🔥 Heatmap: Store vs Sales
+ 
+---
+
+## 🚀 Deployment Steps
+
+**1.**  Deploy SQL schema & sample data (SCRIPT SQL.txt)
+
+**2.**  Upload customers.json to API/Blob for ingestion
+
+**3.**  Configure ADF pipelines:
+
+- Copy data from SQL & JSON to ADLS Bronze
+
+**4.**  Run Databricks notebook (retail projects - multiple tables (1).py)
+
+- Create Silver & Gold tables
+
+**5.**  Connect Power BI → Gold Layer Delta tables
+
+**6.**  Build dashboards as per KPIs
+
+```
+📦 Repository Structure
+├── customers.json                  # Customer data (JSON)
+├── SCRIPT SQL.txt                  # SQL schema + inserts
+├── retail projects - multiple tables (1).py   # Databricks ETL script
+├── docs/
+│   ├── architecture.png            # Architecture diagram
+│   └── dashboard_wireframes.png    # Power BI mockups
+└── README.md                       # Project documentation
+```
+---
+## 📌 Future Enhancements
+
+- Automate pipeline scheduling with ADF triggers
+
+- Add incremental data loads (CDC for transactions)
+
+- Implement role-based security in Power BI
+
+- Integrate ML models for demand forecasting
+
+---