Data Product Architecture

This guide explains how to design and structure data products using DataCards, focusing on architectural principles and composition patterns that make data products effective and maintainable.

What is a Data Product?

A data product is a self-contained system that transforms raw data into actionable insights through interconnected modules. Each module has a specific responsibility and communicates through well-defined interfaces.

Core Principles

Modularity: Focused, single-purpose modules that can be developed and maintained independently
Composability: Modules designed to work together in different combinations
Real-time Reactivity: Automatic response to input changes for live, interactive experiences
Clear Data Flow: Explicit, understandable relationships between modules with clear input/output contracts

T-Shaped Knowledge in Data Products

Effective data product development benefits from T-shaped knowledge - team members with deep expertise in one area (the vertical bar) and broad understanding across multiple domains (the horizontal bar). This enables:

Domain experts to contribute specialized knowledge while understanding technical constraints
Technical specialists to implement solutions while grasping business context
Cross-functional collaboration where different expertise areas work together seamlessly
Better communication between stakeholders with varying technical backgrounds

Data Product Composition Patterns

1. Sequential Pipeline

Linear flow: Input → Processing → Analysis → Results

Use cases: ETL processes, step-by-step workflows
Example: Maritime anchoring analysis (ship data → wind analysis → water analysis → safety recommendations)

2. Parallel Processing

Multiple modules process same input simultaneously:


Input → Module A → Results A
      → Module B → Results B  
      → Module C → Results C

Use cases: Multi-dimensional analysis, comparative studies
Example: Environmental analysis (wind, water, depth processed in parallel)

3. Convergent Analysis

Multiple sources converge into single analysis:


Input A → Processing A ↘
Input B → Processing B → Final Analysis → Decision Support
Input C → Processing C ↗

Use cases: Risk assessment, decision support systems
Example: Safety assessment combining environmental forces, anchor capacity, and operational parameters

4. Hierarchical Processing

Layered modules with higher levels consuming lower-level outputs:

Level 1: Data Collection
Level 2: Basic Processing
Level 3: Advanced Analysis
Level 4: Strategic Insights

Module Types

Input Modules

Purpose: Collect and validate user inputs and external data
Key features: Data validation, user interface, external integration, real-time updates

Processing Modules

Purpose: Transform and analyze data according to business logic
Key features: Data transformation, business rules, algorithms, stateless design

Analysis Modules

Purpose: Apply domain-specific analysis and generate insights
Key features: Domain expertise, complex algorithms, pattern recognition, ML components

Output Modules

Purpose: Present results and recommendations to users
Key features: User-friendly visualization, actionable outputs, decision support

Design Principles

Single Source of Truth: Each data piece has one authoritative source
Explicit Dependencies: Clearly defined and visible module dependencies
Loose Coupling: Modules depend on interfaces, not implementations
High Cohesion: Each module has a single, well-defined responsibility

Design Patterns

Pipeline Pattern

Sequential: Raw Data → Clean Data → Processed Data → Analyzed Data → Insights

Benefits: Easy to understand, clear lineage, simple error handling
Trade-offs: Limited flexibility, sequential bottlenecks

Fan-out/Fan-in Pattern

Parallel: Input → [Module A, B, C] → Aggregator → Output

Benefits: Parallel processing, modular analysis, flexible composition
Trade-offs: Complex coordination, synchronization issues

Event-Driven Pattern

Reactive: Event → Module A → Event → Module B → Event → Module C

Benefits: Real-time responsiveness, loose coupling, scalable
Trade-offs: Complex event management, difficult debugging

Quality Attributes

Reliability: Error handling, data validation, graceful degradation
Performance: Efficient processing, responsive UI, scalable architecture
Maintainability: Clear boundaries, documented interfaces, testable components
Usability: Intuitive UI, clear presentation, actionable insights

Best Practices

Define Clear Interfaces: Well-defined inputs/outputs with clear data contracts
Implement Error Handling: Graceful error handling with meaningful feedback
Use Consistent Naming: Follow consistent patterns for variables, functions, modules
Document Dependencies: Clearly document module requirements and outputs
Design for Testability: Testable in isolation with mock data

Common Anti-patterns to Avoid

God Modules: Trying to do too much, becoming unmaintainable
Tight Coupling: Over-dependence on internal implementations
Data Duplication: Multiple modules maintaining same data in different formats
Implicit Dependencies: Hidden dependencies making system fragile
Monolithic Processing: All logic in single module without separation of concerns

Conclusion

Effective data products combine solid architectural principles with T-shaped team collaboration:

Modular design enables maintainability and reusability
Clear data flows make systems understandable and debuggable
T-shaped knowledge facilitates effective cross-functional collaboration
Well-defined interfaces enable flexible composition and evolution

By following these principles, you can create robust, maintainable data products that provide real value to users.

Maritime Data Product Tutorial - Complete example implementation