What is data-architecture?

This Claude skill provides a comprehensive framework for designing professional-grade data architectures, covering data modeling, pipeline design, and governance. It automates the creation of conceptual, logical, and physical models while generating ERD diagrams and data quality frameworks. Designed for scalability, it supports various data types—including operational, analytical, and streaming—ensuring that data infrastructures are robust, well-documented, and aligned with industry standards like 3NF or denormalized schemas.

When should I use data-architecture?

data-architecture is useful in the following scenarios: • E-commerce Schema Design: Automatically generate ERD diagrams and data models for complex order management and customer relationship systems. • Data Pipeline Architecting: Design end-to-end ETL/ELT workflows for analytical systems, covering ingestion, transformation, and serving layers. • Data Quality & Governance: Establish automated data quality rules (completeness, accuracy, consistency) and governance frameworks for enterprise data domains. • System Modernization: Re-architect legacy data structures into modern streaming or cloud-native architectures with defined volume tiers and validation logic.

name	data-architecture
description	Design data architectures with modeling, pipelines, and governance
version	"2.0.0"
sasmp_version	"1.3.0"
bonded_agent	06-data-architecture
bond_type	PRIMARY_BOND
last_updated	"2025-01"

Data Architecture Skill

Purpose

Design data architectures including data models, pipeline designs, governance frameworks, and quality management for operational and analytical systems.

Parameters

Parameter	Type	Required	Validation	Default
`data_domain`	string	✅	min: 20 chars	-
`design_type`	enum	⚪	model\|pipeline\|governance\|quality	`model`
`data_type`	enum	⚪	operational\|analytical\|streaming	`operational`
`volume_tier`	enum	⚪	small\|medium\|large\|massive	`medium`
`output_format`	enum	⚪	erd\|yaml\|json	`erd`

Execution Flow

┌──────────────────────────────────────────────────────────┐
│ 1. VALIDATE: Check data domain and requirements          │
│ 2. DISCOVER: Identify data sources and entities          │
│ 3. MODEL: Create conceptual/logical/physical model       │
│ 4. DESIGN: Pipeline or governance framework              │
│ 5. QUALITY: Define data quality rules                    │
│ 6. VALIDATE: Check model consistency                     │
│ 7. DOCUMENT: Return data architecture                    │
└──────────────────────────────────────────────────────────┘

Retry Logic

Error	Retry	Backoff	Max Attempts
`VALIDATION_ERROR`	No	-	1
`MODEL_GENERATION_ERROR`	Yes	1s	2
`FORMAT_ERROR`	Yes	500ms	3

Logging & Observability

log_points:
  - event: design_started
    level: info
    data: [design_type, data_type]
  - event: entities_identified
    level: info
    data: [entity_count, relationship_count]
  - event: quality_rules_defined
    level: info
    data: [rule_count, dimensions_covered]

metrics:
  - name: models_created
    type: counter
    labels: [design_type]
  - name: design_time_ms
    type: histogram
  - name: entity_count
    type: gauge

Error Handling

Error Code	Description	Recovery
`E401`	Missing data domain	Request domain description
`E402`	Invalid relationships	Highlight circular/missing refs
`E403`	Schema validation failed	Show validation errors
`E404`	Unsupported volume tier	Suggest architectural changes

Unit Test Template

test_cases:
  - name: "E-commerce data model"
    input:
      data_domain: "E-commerce order management"
      design_type: "model"
      output_format: "erd"
    expected:
      has_entities: true
      entities_include: ["Customer", "Order", "Product"]
      has_relationships: true
      valid_erd: true

  - name: "Analytics pipeline"
    input:
      data_domain: "Customer analytics"
      design_type: "pipeline"
      data_type: "analytical"
    expected:
      has_ingestion: true
      has_transformation: true
      has_serving: true

  - name: "Data quality rules"
    input:
      data_domain: "User profiles"
      design_type: "quality"
    expected:
      has_dimensions: true
      dimensions_include: ["completeness", "accuracy"]
      has_rules: true

Troubleshooting

Common Issues

Symptom	Root Cause	Resolution
Missing relationships	Incomplete domain	Add missing entities
Invalid ERD syntax	Format error	Validate Mermaid ERD
Missing quality rules	Dimensions not specified	Add quality dimensions

Debug Checklist

□ Is data domain clearly defined?
□ Are all entities identified?
□ Are relationships correctly typed?
□ Is output format valid?
□ Are quality dimensions covered?

Data Quality Dimensions

Dimension	Example Rule
Completeness	NOT NULL checks
Accuracy	Regex validation
Consistency	Referential integrity
Timeliness	SLA monitoring
Uniqueness	Primary key constraints

Integration

Component	Trigger	Data Flow
Agent 06	Design request	Receives domain, returns model
Agent 04	Cloud data services	Cloud data platform

Quality Standards

Normalized: 3NF for operational, denormalized for analytical
Documented: All entities and relationships described
Quality-first: DQ rules for all critical fields

Version History

Version	Date	Changes
2.0.0	2025-01	Production-grade: ERD, pipelines, DQ framework
1.0.0	2024-12	Initial release

data-architecture

When & Why to Use This Skill

Use Cases