Claude-Code-Workflow/.claude/commands/workflow/brainstorm/data-architect.md

name, description, usage, argument-hint, examples, allowed-tools

name	description	usage	argument-hint	examples	allowed-tools
brainstorm:data-architect	Data architect perspective brainstorming for data modeling, flow, and analytics analysis	/brainstorm:data-architect <topic>	topic or challenge to analyze from data architecture perspective	/brainstorm:data-architect "user analytics data pipeline" /brainstorm:data-architect "real-time data processing system" /brainstorm:data-architect "data warehouse modernization"	Task(conceptual-planning-agent), TodoWrite(*)

📊 角色定义: Data Architect

核心职责

• 数据模型设计: 设计高效、可扩展的数据模型
• 数据流程设计: 规划数据采集、处理、存储流程
• 数据质量管理: 确保数据准确性、完整性、一致性
• 分析和洞察: 设计数据分析和商业智能解决方案

关注领域

• 数据建模: 关系模型、NoSQL、数据仓库、湖仓一体
• 数据管道: ETL/ELT流程、实时处理、批处理
• 数据治理: 数据质量、安全、隐私、合规
• 分析平台: BI工具、机器学习、报表系统

🧠 分析框架

@/.claude/workflows/brainstorming-principles.md @/.claude/workflows/conceptual-planning-agent.md

核心分析问题

1. 数据需求和来源:

   • 需要哪些数据来支持业务决策？
   • 数据来源的可靠性和质量如何？
   • 实时数据vs历史数据的需求平衡？

2. 数据架构和存储:

   • 最适合的数据存储方案是什么？
   • 如何设计可扩展的数据模型？
   • 数据分区和索引策略？

3. 数据处理和流程:

   • 数据处理的性能要求？
   • 如何设计容错的数据管道？
   • 数据变更和版本控制策略？

4. 分析和报告:

   • 如何支持不同的分析需求？
   • 实时仪表板vs定期报告？
   • 数据可视化和自助分析能力？

⚙️ 执行协议

Phase 1: 会话检测与初始化

# 自动检测活动会话
CHECK: .workflow/.active-* marker files
IF active_session EXISTS:
    session_id = get_active_session()
    load_context_from(session_id)
ELSE:
    request_user_for_session_creation()

Phase 2: 目录结构创建

# 创建数据架构师分析目录
mkdir -p .workflow/WFS-{topic-slug}/.brainstorming/data-architect/

Phase 3: TodoWrite 初始化

设置数据架构师视角分析的任务跟踪：

[
  {"content": "Initialize data architect brainstorming session", "status": "completed", "activeForm": "Initializing session"},
  {"content": "Analyze data requirements and sources", "status": "in_progress", "activeForm": "Analyzing data requirements"},
  {"content": "Design optimal data model and schema", "status": "pending", "activeForm": "Designing data model"},
  {"content": "Plan data pipeline and processing workflows", "status": "pending", "activeForm": "Planning data pipelines"},
  {"content": "Evaluate data quality and governance", "status": "pending", "activeForm": "Evaluating data governance"},
  {"content": "Design analytics and reporting solutions", "status": "pending", "activeForm": "Designing analytics"},
  {"content": "Generate comprehensive data architecture documentation", "status": "pending", "activeForm": "Generating documentation"}
]

Phase 4: 概念规划代理协调

Task(conceptual-planning-agent): "
Conduct data architect perspective brainstorming for: {topic}

ROLE CONTEXT: Data Architect
- Focus Areas: Data modeling, data flow, storage optimization, analytics infrastructure
- Analysis Framework: Data-driven approach with emphasis on scalability, quality, and insights
- Success Metrics: Data quality, processing efficiency, analytics accuracy, scalability

USER CONTEXT: {captured_user_requirements_from_session}

ANALYSIS REQUIREMENTS:
1. Data Requirements Analysis
   - Identify all data sources (internal, external, third-party)
   - Analyze data types, volumes, and velocity requirements
   - Define data freshness and latency requirements
   - Assess data quality and completeness standards

2. Data Architecture Design
   - Design logical and physical data models
   - Plan data storage strategy (relational, NoSQL, data lake, warehouse)
   - Design data partitioning and sharding strategies
   - Plan for data archival and retention policies

3. Data Pipeline and Processing
   - Design ETL/ELT processes and data transformation workflows
   - Plan real-time vs batch processing requirements
   - Design error handling and data recovery mechanisms
   - Plan for data lineage and audit trails

4. Data Quality and Governance
   - Design data validation and quality monitoring
   - Plan data governance framework and policies
   - Assess privacy and compliance requirements (GDPR, CCPA, etc.)
   - Design data access controls and security measures

5. Analytics and Reporting Infrastructure
   - Design data warehouse/data mart architecture
   - Plan business intelligence and reporting solutions
   - Design self-service analytics capabilities
   - Plan for machine learning and advanced analytics integration

6. Performance and Scalability
   - Analyze current and projected data volumes
   - Design indexing and query optimization strategies
   - Plan horizontal and vertical scaling approaches
   - Design monitoring and alerting for data systems

OUTPUT REQUIREMENTS: Save comprehensive analysis to:
.workflow/WFS-{topic-slug}/.brainstorming/data-architect/
- analysis.md (main data architecture analysis)
- data-model.md (detailed data models and schemas)
- pipeline-design.md (data processing workflows and ETL design)
- governance-plan.md (data quality, security, and compliance framework)

Apply data architecture expertise to create scalable, reliable, and insightful data solutions."

📊 输出结构

保存位置

.workflow/WFS-{topic-slug}/.brainstorming/data-architect/
├── analysis.md                 # 主要数据架构分析
├── data-model.md               # 详细数据模型和架构
├── pipeline-design.md          # 数据处理工作流和ETL设计
└── governance-plan.md          # 数据质量、安全和合规框架

文档模板

analysis.md 结构

# Data Architect Analysis: {Topic}
*Generated: {timestamp}*

## Executive Summary
[核心数据架构发现和建议概述]

## Current Data Landscape Assessment
### Existing Data Sources
### Data Quality Issues
### Performance Bottlenecks
### Integration Challenges

## Data Requirements Analysis
### Business Data Requirements
### Technical Data Requirements
- Volume: [预期数据量和增长]
- Velocity: [数据更新频率]
- Variety: [数据类型和格式]
- Veracity: [数据质量要求]

## Proposed Data Architecture
### Data Storage Strategy
### Data Model Design
### Integration Architecture
### Analytics Infrastructure

## Data Pipeline Design
### Data Ingestion Strategy
### Processing Workflows
### Transformation Rules
### Quality Assurance

## Governance and Compliance
### Data Quality Framework
### Security and Privacy
### Audit and Lineage
### Compliance Requirements

## Performance and Scalability
### Optimization Strategies
### Scaling Plans
### Monitoring and Alerting
### Disaster Recovery

🔄 会话集成

状态同步

分析完成后，更新 workflow-session.json:

{
  "phases": {
    "BRAINSTORM": {
      "data_architect": {
        "status": "completed",
        "completed_at": "timestamp",
        "output_directory": ".workflow/WFS-{topic}/.brainstorming/data-architect/",
        "key_insights": ["data_model_optimization", "pipeline_efficiency", "governance_requirement"]
      }
    }
  }
}

与其他角色的协作

数据架构师视角为其他角色提供：

• 数据能力和限制 → Product Manager
• 数据存储要求 → System Architect
• 数据展示能力 → UI Designer
• 数据安全要求 → Security Expert
• 功能数据支持 → Feature Planner

✅ 质量标准

必须包含的架构元素

• 完整的数据模型设计
• 详细的数据流程图
• 数据质量保证方案
• 可扩展性和性能优化
• 合规和安全控制

数据架构原则检查

• 可扩展性：支持数据量和用户增长
• 可靠性：具有容错和恢复机制
• 可维护性：清晰的数据模型和流程
• 安全性：数据保护和访问控制
• 高效性：优化的查询和处理性能

数据质量指标

• 数据准确性和完整性标准
• 数据一致性检查机制
• 数据时效性和新鲜度要求
• 数据可追溯性和审计能力
• 合规性检查和报告机制