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Multi-Lab Design

Overview

The Multi-Lab Design organizes a data engineering playground into multiple independent labs, each representing a self-contained ETL pipeline or analytical experiment. This design ensures modularity, safety, and flexibility, allowing teams or individuals to experiment without affecting other projects.

Goals

  • Isolation: Each lab operates independently with its own datasets, models, and transformations.
  • Modularity: Labs can be added, removed, or updated without impacting the broader system.
  • Reusability: Shared components or datasets can be referenced across labs while maintaining separation of experimental data.

Lab Structure

Typical Lab Folder

lab1_sales_performance/
├── raw/
├── bronze/
├── silver/
├── gold/
├── dbt/
│ ├── models/
│ ├── snapshots/
│ └── seeds/
└── README.md
  • raw: Ingested source data (unchanged).
  • bronze: Cleaned, minimally transformed data.
  • silver: Enriched and validated datasets.
  • gold: Business-ready, aggregated tables for analysis or reporting.
  • dbt: Contains lab-specific transformations and metadata.

Shared vs Lab-Specific

  • Lab-specific datasets: Only relevant to a single lab; changes are isolated.
  • Shared datasets: Common reference tables or utilities used across multiple labs, stored in a central shared/ directory.

💡 Tip: Only include data in shared/ if multiple labs depend on it. Avoid making everything shared to prevent accidental coupling.

Benefits of Multi-Lab Design

  • Safe experimentation: Teams can iterate freely without breaking other pipelines.
  • Scalability: Easily add new labs for different datasets or analytical scenarios.
  • Reproducibility: Clear boundaries make it easy to reproduce experiments or roll back changes.
  • Collaboration: Multiple contributors can work in parallel without conflicts.

Best Practices

  1. Maintain consistent folder structures across labs.
  2. Clearly document each lab’s purpose in its README.
  3. Version shared datasets and transformations carefully.
  4. Use naming conventions to avoid collisions (e.g., lab1_sales_2025_bronze).
  5. Leverage automation (e.g., CI/CD pipelines) to validate transformations before deployment.

Example Diagram

+----------------+     +----------------+     +----------------+
|   Lab 1        |     |   Lab 2        |     |   Lab 3        |
|   raw → bronze |     | raw → bronze   |     | raw → bronze   |
|   → silver     |     | → silver       |     | → silver       |
|   → gold       |     | → gold         |     | → gold         |
+----------------+     +----------------+     +----------------+
         \                   |                     /
          \                  |                    /
           \                 |                   /
            \                |                  /
             \               |                 /
              +--------------------------------+
              |           Shared Data          |
              +--------------------------------+
This diagram shows independent labs consuming shared datasets while maintaining isolated pipelines.

Summary

The Multi-Lab Design is ideal for learning, experimentation, and scalable analytics. By enforcing modularity and isolation, it enables reproducible workflows and safe collaboration while maintaining the flexibility to share data and reusable transformations across projects.