🏗️ Iceberg Data Engineering Platform¶

📋 Project Overview¶

This project implements a modern lakehouse architecture using open-source technologies, demonstrating how to build a scalable, efficient, and cost-effective data platform. The lakehouse paradigm combines the best features of data lakes and data warehouses, enabling efficient data storage, processing, and analytics.

🎯 Key Features¶

• Multi-domain Data Ingestion: Real estate, flight radar, and e-commerce data sources
• ACID Transactions: Using Apache Iceberg for reliable data management
• Distributed Processing: Apache Spark for scalable data transformations
• SQL Analytics: Trino for high-performance querying
• Data Orchestration: Dagster for workflow management
• Business Intelligence: Apache Superset for visualization and dashboards
• Security: Apache Ranger for access control and governance

🛠️ Technology Stack¶

Core Data Platform¶

• Apache Iceberg - Table format for ACID transactions, schema evolution, and time travel
• Apache Spark - Distributed data processing engine
• Trino - High-performance distributed SQL query engine
• MinIO - S3-compatible object storage

Data Engineering Tools¶

• dbt - Data transformation and modeling
• Dagster - Data orchestration and workflow management
• Apache Superset - Data visualization and BI platform

Infrastructure & Security¶

• PostgreSQL - Metadata storage and configuration
• Hive Metastore - Table catalog and metadata management
• Apache Ranger - Security and access control
• Hue - SQL query interface

Development & Testing¶

• Docker - Containerization and deployment
• pytest - Testing framework
• pre-commit - Code quality hooks

🚀 Quick Start¶

Prerequisites¶

• Docker and Docker Compose
• Git

1. Clone and Setup¶

git clone <repository-url>
cd iceberg_data_engineering

2. Start the Platform¶

docker-compose up -d

3. Access Services¶

📊 Data Domains¶

🏠 Asset Property¶

• Source: Real estate sales data
• Pipeline: asset_property_pipeline.py
• Models: Property sales, market trends

✈️ Flight Radar¶

• Source: FlightRadar24 API
• Pipeline: flight_radar_pipeline.py
• Models: Airlines, airports, flight data

🛒 E-commerce¶

• Source: Mock user product data
• Pipeline: ecommerce_pipeline.py
• Models: Customer orders, product analytics

🔄 Data Flow¶

graph TD
    A[External APIs] --> B[Dagster Orchestration]
    B --> C[Data Ingestion Ops]
    C --> D[MinIO Bronze Layer]
    D --> E[Data Preparation Ops]
    E --> F[MinIO Prepare Layer]
    F --> G[Delta Tables]
    G --> H[Success Sensor]
    H --> I[dbt Transformations]
    I --> J[MinIO Warehouse Layer]
    J --> K[Iceberg Tables]

    L[Hive Metastore] --> G
    M[Apache Ranger] --> I
    N[PostgreSQL] --> L

    subgraph "Storage Buckets"
        O[datalake/bronze: Raw Data]
        P[datalake/prepare: Delta Tables]
        Q[warehouse: Iceberg Tables]
    end

    subgraph "Trino Catalogs"
        R[spark_catalog: Preparation Tasks]
        S[flight_radar: Flight Analytics]
        T[ecommerce: E-commerce Analytics]
        U[asset_property: Property Analytics]
    end

    D --> O
    F --> P
    J --> Q

    G --> R
    K --> S
    K --> T
    K --> U

Data Pipeline Architecture¶

1. Data Ingestion: External APIs → CSV/JSON/Parquet files in datalake/bronze
2. Data Preparation: Bronze files → Delta tables in datalake/prepare
3. Data Transformation: Delta tables → dbt models → Iceberg tables in warehouse
4. Analytics: Domain-specific Trino catalogs for querying Iceberg tables
5. Orchestration: Dagster manages the entire pipeline with sensors and schedules

🏗️ Architecture Components¶

Data Ingestion Layer¶

• Custom Python modules for API data collection
• MinIO Bronze Storage: Raw data stored as CSV/JSON/Parquet in datalake/bronze
• Schema validation and data quality checks
• Multi-format support: CSV, JSON, Parquet file outputs

Data Preparation Layer¶

• Apache Spark for distributed data processing
• MinIO Prepare Storage: Processed data stored as Delta tables in datalake/prepare
• Delta Lake Integration: ACID transactions and schema evolution
• Data cleaning and standardization

Data Transformation Layer¶

• dbt transformations for business logic and analytical models
• Success sensors trigger dbt jobs after preparation completion
• Iceberg table generation in warehouse bucket
• Domain-specific catalogs for analytical querying
• Analytical model creation from prepared Delta tables

Analytics Layer¶

• Trino for SQL analytics on Delta and Iceberg tables
• Domain-specific catalogs: spark_catalog, flight_radar, ecommerce, asset_property
• Apache Superset for visualization and dashboards
• Hue for ad-hoc queries

Security & Governance¶

• Apache Ranger for access control
• Hive Metastore for metadata management
• PostgreSQL for configuration storage

🚧 Roadmap¶

• CI/CD Pipeline for GCP deployment
• Data Quality Monitoring with Great Expectations
• Streaming Data with Apache Kafka
• ML Pipeline integration
• Multi-cloud deployment options

🤝 Contributing¶

See Development Guide for detailed contribution instructions.

📄 License¶

This project is licensed under the MIT License - see the LICENSE file for details.