🏗️ Data Architecture Project

End-to-end Data Engineering and Data Architecture project demonstrating ETL pipelines, SQL analytics, NoSQL database operations, and enterprise data warehouse design.

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🚀 Project Overview

This project simulates a real-world retail data engineering environment where raw business data is processed, transformed, stored, and analyzed using multiple database architectures.

The system covers the complete data lifecycle:

• Data Ingestion
• ETL Processing
• Relational Database Design
• SQL Analytics
• NoSQL Data Storage
• Data Warehouse Modeling
• Business Intelligence Queries

The objective is to demonstrate how modern organizations manage structured and semi-structured data across different storage systems while enabling scalable analytics and reporting.

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🎯 Business Scenario

A retail company generates large volumes of data from:

• Customers
• Products
• Sales Transactions
• Inventory Systems
• Business Operations

The organization requires:

• Efficient data storage
• Fast analytical queries
• Historical reporting
• Scalable architecture
• Multi-database integration

This project designs an architecture capable of handling these requirements.

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🏛️ Architecture Components

Part 1 — ETL & Relational Database

Responsible for:

• Data Extraction
• Data Cleaning
• Data Transformation
• Relational Storage
• Business SQL Queries

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Part 2 — NoSQL Database

Implements:

• MongoDB Operations
• Document-Based Storage
• Product Catalog Management
• Flexible Data Structures

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Part 3 — Data Warehouse

Implements:

• Star Schema Modeling
• Fact Tables
• Dimension Tables
• Analytical Query Processing
• Business Intelligence Reporting

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🧠 Data Flow Architecture

Raw Data Sources
        │
        ▼
Data Extraction
        │
        ▼
ETL Pipeline
        │
        ▼
Relational Database
        │
        ├──────────────┐
        ▼              ▼
SQL Analytics      MongoDB Storage
        │              │
        └──────┬───────┘
               ▼
        Data Warehouse
               │
               ▼
      Business Intelligence

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⚙️ Technology Stack

Programming

• Python

Databases

• SQL
• MongoDB

Data Engineering

• ETL Pipelines
• Data Transformation
• Data Cleaning

Analytics

• SQL Queries
• Business Reporting
• Warehouse Analytics

Tools

• Git
• GitHub
• VS Code

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📂 Project Structure

data-architecture-project/
│
├── data/
│
├── part1-database-etl/
│   ├── ETL Pipeline
│   ├── SQL Scripts
│   └── Database Operations
│
├── part2-nosql/
│   ├── MongoDB Operations
│   └── Product Catalog Data
│
├── part3-datawarehouse/
│   ├── Warehouse Schema
│   ├── Fact Tables
│   └── Dimension Tables
│
├── README.md
└── requirements.txt

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📊 Key Features

ETL Processing

• Data Extraction
• Data Cleaning
• Data Transformation
• Data Loading

SQL Analytics

• Business Queries
• Aggregations
• Reporting
• Relational Modeling

NoSQL Operations

• Document Databases
• Flexible Data Models
• MongoDB Collections

Data Warehouse Design

• Star Schema
• Fact Tables
• Dimension Tables
• Analytical Processing

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📈 Engineering Concepts Demonstrated

• Data Architecture
• Database Design
• ETL Development
• Data Modeling
• Data Warehousing
• SQL Optimization
• NoSQL Databases
• Business Intelligence
• Enterprise Data Pipelines

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🌍 Real-World Applications

This architecture can be adapted for:

• Retail Analytics
• E-Commerce Platforms
• Supply Chain Systems
• Customer Intelligence Platforms
• Sales Reporting Systems
• Business Intelligence Dashboards

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🎓 Learning Outcomes

This project demonstrates practical knowledge of:

• Data Engineering
• Database Architecture
• Relational Databases
• NoSQL Systems
• ETL Workflows
• Data Warehousing
• Analytics Engineering

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👨‍💻 Author

Arjun R K

GitHub: https://github.com/AxArjun

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📜 License

MIT License