📦 Batch Architecture Patterns¶

Reference architectures and patterns for batch data processing workloads.

🎯 Overview¶

Batch processing handles large volumes of data in scheduled intervals, ideal for:

• ETL/ELT pipelines: Transforming and loading data into analytics systems
• Data warehousing: Building dimensional models for reporting
• Historical analysis: Processing accumulated data for insights
• Machine learning: Training models on large datasets

📊 Pattern Catalog¶

Data Warehouse Patterns¶

Classic dimensional modeling and modern lakehouse approaches.

Lambda Architecture¶

Combining batch and real-time processing layers.

graph LR
    subgraph "Data Sources"
        S[Event Stream]
    end

    subgraph "Speed Layer"
        SP[Stream Analytics]
    end

    subgraph "Batch Layer"
        B1[Data Lake]
        B2[Spark Processing]
    end

    subgraph "Serving Layer"
        SV[Query Engine]
    end

    S --> SP
    S --> B1
    B1 --> B2
    SP --> SV
    B2 --> SV

Kappa Architecture¶

Simplified architecture using stream processing for both real-time and batch.

graph LR
    S[Event Stream] --> K[Kafka/Event Hubs]
    K --> P[Stream Processor]
    P --> ST[Data Lake]
    ST --> Q[Query Layer]

🏗️ Reference Architecture¶

Modern Data Warehouse¶

graph TB
    subgraph "Sources"
        S1[Operational DBs]
        S2[Files/APIs]
        S3[SaaS Apps]
    end

    subgraph "Ingestion"
        I1[Data Factory]
    end

    subgraph "Storage"
        L1[Bronze Layer<br/>Raw Data]
        L2[Silver Layer<br/>Cleansed]
        L3[Gold Layer<br/>Curated]
    end

    subgraph "Processing"
        P1[Synapse Spark]
        P2[Databricks]
    end

    subgraph "Serving"
        SV1[Synapse SQL]
        SV2[Power BI]
    end

    S1 --> I1
    S2 --> I1
    S3 --> I1
    I1 --> L1
    L1 --> P1
    L1 --> P2
    P1 --> L2
    P2 --> L2
    L2 --> L3
    L3 --> SV1
    SV1 --> SV2

📚 Related Documentation¶

• Streaming Architectures
• Hybrid Architectures
• Implementation Guides

Last Updated: January 2025