🚗 Databricks Car Project – Data Engineering Pipeline

📌 Overview

This project demonstrates an end-to-end Data Engineering pipeline built on Databricks. It showcases modern ETL/ELT practices using Delta Lake, Delta Live Tables (DLT), Auto Loader, and Unity Catalog.

The solution follows the Medallion Architecture (Bronze → Silver → Gold) to ensure scalable, reliable, and high-quality data processing.

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🏗️ Architecture

🔷 High-Level Architecture

        Source Data (CSV/JSON/Streaming)
                    │
                    ▼
            Auto Loader (Ingestion)
                    │
                    ▼
              Bronze Layer
        (Raw, Incremental Data)
                    │
                    ▼
         Silver Layer (Cleaned Data)
                    │
                    ▼
      Gold Layer (Aggregated Data)
                    │
                    ▼
        Analytics / Reporting / BI

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⚙️ Tech Stack

• Databricks
• Apache Spark (PySpark)
• Delta Lake
• Delta Live Tables (DLT)
• Unity Catalog
• Auto Loader

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📂 Project Structure

Databricks-Carproject/
│
├── Autoloader/                # Data ingestion scripts
├── DAC/                       # Data access and governance
├── DELTA LIVE TABLE/
│   ├── DLT_Demo_1/
│   ├── DLT_END_TO_END/
│
├── explorations/              # Data exploration notebooks
├── transformations/           # Business logic and transformations
├── utilities/                 # Helper functions and reusable code
│
├── bronze_layer/              # Raw data layer
├── silver_layer/              # Cleaned & transformed data
├── README.md

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🔄 Data Pipeline Flow

1️⃣ Data Ingestion

• Uses Auto Loader for incremental file ingestion
• Supports structured and semi-structured data
• Schema inference and evolution enabled

2️⃣ Bronze Layer

• Stores raw ingested data
• Append-only operations
• Maintains data history

3️⃣ Silver Layer

• Data cleaning and transformation
• Handles null values, duplicates, and schema consistency
• Applies business rules

4️⃣ Gold Layer (Optional/Extendable)

• Aggregated and business-level datasets
• Ready for reporting and analytics

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🚀 Key Features

• Scalable ETL pipelines using Spark
• Incremental data processing with Auto Loader
• Data quality and pipeline management using Delta Live Tables
• Centralized governance using Unity Catalog
• Optimized storage with Delta Lake (ACID transactions)

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📊 Optimization Techniques

• File compaction (OPTIMIZE)
• Partitioning strategies
• Z-order indexing
• Adaptive Query Execution (AQE)

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🔐 Data Governance

• Unity Catalog for centralized access control
• Role-based access
• Data lineage tracking

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▶️ How to Run

1. Upload notebooks to Databricks workspace
2. Configure cluster
3. Set up Unity Catalog (if required)
4. Run Auto Loader for ingestion
5. Execute DLT pipelines

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📌 Future Enhancements

• Add Gold layer dashboards
• Integrate with Power BI / Tableau
• Implement CI/CD pipeline
• Add real-time streaming use cases

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👨‍💻 Author

Naman Singhal

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⭐ Acknowledgements

This project is built for learning and demonstrating real-world Data Engineering concepts using Databricks.