Architecture Components¶

This document provides a detailed overview of the architectural components that make up the Iceberg Data Engineering Platform.

System Architecture¶

graph TB
    subgraph "Data Ingestion Layer"
        A[External APIs]
        B[Custom Python Modules]
        C[MinIO Bronze Storage]
    end

    subgraph "Data Processing Layer"
        D[Apache Spark]
        E[MinIO Silver Storage]
        F[Delta Lake]
    end

    subgraph "Data Transformation Layer"
        G[dbt Transformations]
        H[MinIO Gold Storage]
        I[Apache Iceberg]
    end

    subgraph "Analytics Layer"
        J[Trino Query Engine]
        K[Apache Superset]
        L[Hue SQL Interface]
    end

    subgraph "Orchestration Layer"
        M[Dagster]
        N[Success Sensors]
        O[Workflow Management]
    end

    subgraph "Security & Governance"
        P[Apache Ranger]
        Q[Hive Metastore]
        R[PostgreSQL]
    end

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I
    I --> J
    J --> K
    J --> L

    M --> B
    M --> D
    M --> G
    N --> G

    P --> I
    Q --> F
    Q --> I
    R --> Q

Core Components¶

Data Ingestion Layer¶

External APIs¶

• Real Estate APIs: Property sales data
• FlightRadar24 API: Flight tracking data
• Mock E-commerce Data: Customer and order data

Custom Python Modules¶

• Asset Property Module: Real estate data collection
• Flight Radar Module: Flight data ingestion
• E-commerce Module: Mock data generation

MinIO Bronze Storage¶

• Purpose: Raw data storage
• Format: CSV, JSON, Parquet
• Location: datalake/bronze bucket
• Characteristics: Immutable, schema-on-read

Data Processing Layer¶

Apache Spark¶

• Purpose: Distributed data processing
• Configuration: Master-worker architecture
• Resources: Configurable cores and memory
• Features: SQL, DataFrame API, Streaming

MinIO Silver Storage¶

• Purpose: Processed data storage
• Format: Delta Lake (Parquet + metadata)
• Location: datalake/silver bucket
• Characteristics: ACID transactions, schema evolution

Delta Lake¶

• Features: ACID transactions, time travel, schema evolution
• Optimization: Z-ordering, compaction
• Monitoring: Data quality checks, lineage tracking

Data Transformation Layer¶

dbt Transformations¶

• Purpose: Business logic and analytical models
• Features: SQL-based transformations, testing, documentation
• Models: Staging, intermediate, marts
• Testing: Data quality, referential integrity

MinIO Gold Storage¶

• Purpose: Analytics-ready data
• Format: Apache Iceberg (Parquet + metadata)
• Location: warehouse bucket
• Characteristics: High performance, ACID transactions

Apache Iceberg¶

• Features: Schema evolution, time travel, partition evolution
• Optimization: File pruning, predicate pushdown
• Compatibility: Multiple query engines

Analytics Layer¶

Trino Query Engine¶

• Purpose: High-performance SQL analytics
• Catalogs: Domain-specific data access
• Features: Distributed query execution, connector framework
• Performance: Query optimization, parallel processing

Apache Superset¶

• Purpose: Business intelligence and visualization
• Features: Dashboards, charts, SQL lab
• Data Sources: Trino connectors
• Security: Role-based access control

Hue SQL Interface¶

• Purpose: Ad-hoc SQL queries
• Features: Query editor, result visualization
• Connections: Trino, Spark SQL
• User Management: Authentication and authorization

Orchestration Layer¶

Dagster¶

• Purpose: Data orchestration and workflow management
• Features: Asset-based pipelines, sensors, schedules
• Monitoring: Pipeline execution, data quality
• UI: Web interface for pipeline management

Success Sensors¶

• Purpose: Trigger downstream processes
• Triggers: Data availability, quality checks
• Actions: Start dbt transformations, send notifications

Workflow Management¶

• Features: Dependency management, error handling
• Monitoring: Execution logs, performance metrics
• Alerting: Failure notifications, performance warnings

Security & Governance¶

Apache Ranger¶

• Purpose: Security and access control
• Features: Policy management, audit logging
• Integration: Hive, Spark, Trino
• Governance: Data classification, access policies

Hive Metastore¶

• Purpose: Metadata management
• Features: Schema registry, table metadata
• Integration: Spark, Trino, dbt
• Storage: PostgreSQL backend

PostgreSQL¶

• Purpose: Metadata and configuration storage
• Databases: Hive metastore, Ranger, Hue
• Features: ACID compliance, backup/recovery
• Performance: Connection pooling, query optimization

Component Interactions¶

Data Flow¶

1. Ingestion: External APIs → Custom modules → Bronze storage
2. Processing: Bronze data → Spark processing → Silver storage
3. Transformation: Silver data → dbt transformations → Gold storage
4. Analytics: Gold data → Trino queries → Superset dashboards

Orchestration Flow¶

1. Scheduling: Dagster schedules data ingestion
2. Execution: Custom modules execute data collection
3. Processing: Spark processes raw data
4. Transformation: dbt applies business logic
5. Monitoring: Success sensors trigger downstream processes

Security Flow¶

1. Authentication: Users authenticate via Ranger
2. Authorization: Policies control data access
3. Audit: All access logged for compliance
4. Governance: Data classification and lineage tracking

Configuration¶

Environment Variables¶

# Database Configuration
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgrespassword
HIVE_DB_USER=hiveuser
HIVE_DB_PASSWORD=hivepassword

# MinIO Configuration
MINIO_ROOT_USER=minioadmin
MINIO_ROOT_PASSWORD=minioadmin
MINIO_ENDPOINT=http://minio:9000

# Spark Configuration
SPARK_MASTER_URL=spark://spark-master:7077
SPARK_WORKER_CORES=2
SPARK_WORKER_MEMORY=1G

# Trino Configuration
TRINO_COORDINATOR=true
TRINO_HTTP_PORT=8080

Service Ports¶

Scalability Considerations¶

Horizontal Scaling¶

• Spark Workers: Add more worker nodes
• Trino Workers: Scale query execution
• MinIO: Distributed storage clusters
• PostgreSQL: Read replicas

Vertical Scaling¶

• Memory: Increase worker memory
• CPU: Add more cores
• Storage: Increase disk capacity
• Network: Optimize bandwidth

Performance Optimization¶

• Caching: Query result caching
• Partitioning: Data partitioning strategies
• Indexing: Query-specific indexes
• Compression: Data compression algorithms

Monitoring and Observability¶

Metrics Collection¶

• System Metrics: CPU, memory, disk usage
• Application Metrics: Query performance, data quality
• Business Metrics: Data freshness, processing latency

Logging¶

• Application Logs: Service-specific logs
• Audit Logs: Security and access logs
• Error Logs: Exception and failure logs

Alerting¶

• Threshold Alerts: Performance degradation
• Failure Alerts: Service failures
• Quality Alerts: Data quality issues

Disaster Recovery¶

Backup Strategy¶

• Database Backups: PostgreSQL regular backups
• Data Backups: MinIO data replication
• Configuration Backups: Service configurations

Recovery Procedures¶

• Service Recovery: Automated service restart
• Data Recovery: Point-in-time recovery
• Configuration Recovery: Infrastructure as code

Security Considerations¶

Network Security¶

• Firewall Rules: Port access control
• VPN Access: Secure remote access
• SSL/TLS: Encrypted communications

Data Security¶

• Encryption: Data at rest and in transit
• Access Control: Role-based permissions
• Audit Trail: Comprehensive logging

Compliance¶

• Data Privacy: GDPR compliance
• Data Retention: Automated data lifecycle
• Access Logging: Audit trail maintenance