🚀 Azure End-to-End E-Commerce Data Platform

Medallion Architecture with Azure Databricks

📌 Project Overview

This project demonstrates a production-grade end-to-end data engineering pipeline for e-commerce data using Microsoft Azure and Databricks Medallion Architecture.
It showcases how raw data is ingested, transformed, and served for analytics using cloud-native, scalable, and enterprise-ready design patterns.

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🏗 Architecture Overview

The platform follows a layered Medallion Architecture:

graph TD
    %% Define Styles with Black Text
    classDef source fill:#FFD1DC,stroke:#333,stroke-width:2px,color:#000;
    classDef landing fill:#FFFACD,stroke:#d4a017,stroke-width:2px,color:#000;
    classDef bronze fill:#E6CCB2,stroke:#333,stroke-width:2px,color:#000;
    classDef silver fill:#E5E4E2,stroke:#333,stroke-width:2px,color:#000;
    classDef gold fill:#FFD700,stroke:#333,stroke-width:2px,color:#000;
    classDef viz fill:#D6EAF8,stroke:#333,stroke-width:2px,color:#000;

    %% Pipeline Flow
    S[(Source CSV Files)]:::source --> L1[Landing Zone 1: Raw CSV]:::landing
    L1 --> L2[Landing Zone 2: Parquet]:::landing
    L2 --> B[Bronze Layer: Raw Delta]:::bronze
    B --> Si[Silver Layer: Cleaned & Transformed]:::silver
    Si --> G[Gold Layer: Analytics-Ready]:::gold
    G --> V{Analytics & Dashboards}:::viz

Loading

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🔹 Data Flow Explanation

1️⃣ Data Ingestion – Landing Zone 1

• Raw CSV files are manually uploaded into Azure Data Lake Storage Gen2
• Data is stored as-is (schema-on-read)
• Acts as a system of record for replay and audit

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2️⃣ CSV → Parquet Conversion – Landing Zone 2

• Azure Data Factory (ADF) pipeline converts CSV files into Parquet
• Improves:
  • Query performance
  • Compression efficiency
  • Downstream processing speed

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3️⃣ Bronze Layer (Databricks)

• Reads Parquet files from Landing Zone 2
• Performs append-only ingestion into Delta tables
• No transformations applied
• Preserves raw structure and ingestion metadata

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4️⃣ Silver Layer (Databricks)

• Cleans and standardizes data
• Handles:
  • Null values
  • Duplicates
  • Data type corrections
• Applies business-level transformations
• Implements efficient incremental processing

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5️⃣ Gold Layer (Databricks)

• Creates business-ready, analytics-optimized tables
• Aggregates and joins cleaned data
• Serves as the single source of truth for reporting

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🔁 Pipeline Orchestration

• Event-based triggers activate pipelines when new data arrives
• Single trigger with conditional processing ensures efficiency
• Prevents unnecessary recomputation of static datasets

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📊 Analytics & Consumption

• Azure Synapse Analytics used for SQL-based analytics
• Databricks Dashboards for interactive visualizations
• Gold layer tables are queried directly for insights

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🛠 Tech Stack

• Azure Data Factory
• Azure Data Lake Storage Gen2
• Azure Databricks
• Delta Lake
• Parquet
• Azure Synapse Analytics

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🎯 Key Learnings

• Designing scalable cloud data architectures
• Implementing Medallion Architecture in Databricks
• Building event-driven ETL pipelines
• Optimizing storage and analytics using Parquet & Delta Lake
• Applying real-world data engineering best practices

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🚀 Future Enhancements

• Implement Auto Loader for streaming ingestion
• Add data quality checks and validations
• Integrate Power BI dashboards
• Enable CI/CD for Databricks workflows

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🤝 Connect

If you’re interested in Data Engineering, Azure, or Databricks, feel free to connect or explore the notebooks in this repository.