Loading...
Loading...
Compare original and translation side by side
Star Schema Compliance:
□ Clear separation of fact and dimension tables
□ Proper grain consistency within fact tables
□ Dimension tables contain descriptive attributes
□ Minimal snowflaking (justified when present)
□ Appropriate use of bridge tables for many-to-many
Table Design Quality:
□ Meaningful table and column names
□ Appropriate data types for all columns
□ Proper primary and foreign key relationships
□ Consistent naming conventions
□ Adequate documentation and descriptions星型架构合规性:
□ 事实表与维度表明确分离
□ 事实表内粒度保持一致
□ 维度表包含描述性属性
□ 最小化雪花型结构（若存在需合理说明）
□ 正确使用桥接表处理多对多关系
表设计质量:
□ 有意义的表名和列名
□ 所有列使用合适的数据类型
□ 正确设置主键和外键关系
□ 一致的命名规范
□ 充分的文档和描述Relationship Quality Assessment:
□ Correct cardinality settings (1:*, *:*, 1:1)
□ Appropriate filter directions (single vs. bidirectional)
□ Referential integrity settings optimized
□ Hidden foreign key columns from report view
□ Minimal circular relationship paths
Performance Considerations:
□ Integer keys preferred over text keys
□ Low-cardinality relationship columns
□ Proper handling of missing/orphaned records
□ Efficient cross-filtering design
□ Minimal many-to-many relationships关系质量评估:
□ 正确设置基数（1:*, *:*, 1:1）
□ 选择合适的筛选方向（单向 vs 双向）
□ 优化参照完整性设置
□ 报表视图中隐藏外键列
□ 最小化循环关系路径
性能考量:
□ 优先使用整数键而非文本键
□ 低基数的关系列
□ 正确处理缺失/孤立记录
□ 高效的交叉筛选设计
□ 最小化多对多关系Storage Mode Optimization:
□ Import mode used appropriately for small-medium datasets
□ DirectQuery implemented properly for large/real-time data
□ Composite models designed with clear strategy
□ Dual storage mode used effectively for dimensions
□ Hybrid mode applied appropriately for fact tables
Performance Alignment:
□ Storage modes match performance requirements
□ Data freshness needs properly addressed
□ Cross-source relationships optimized
□ Aggregation strategies implemented where beneficial存储模式优化:
□ 导入模式适用于中小型数据集
□ DirectQuery模式针对大型/实时数据正确实施
□ 复合模型设计具备清晰策略
□ 维度表有效使用双存储模式
□ 事实表合理应用混合模式
性能匹配:
□ 存储模式符合性能要求
□ 正确满足数据新鲜度需求
□ 跨源关系已优化
□ 合理实施聚合策略Evaluate Model Structure:
Fact Table Analysis:
- Grain definition and consistency
- Appropriate measure columns
- Foreign key completeness
- Size and growth projections
- Historical data management
Dimension Table Analysis:
- Attribute completeness and quality
- Hierarchy design and implementation
- Slowly changing dimension handling
- Surrogate vs. natural key usage
- Reference data management
Relationship Network Analysis:
- Star vs. snowflake patterns
- Relationship complexity assessment
- Filter propagation paths
- Cross-filtering impact evaluation评估模型结构:
事实表分析:
- 粒度定义与一致性
- 合理的度量值列
- 外键完整性
- 大小与增长预测
- 历史数据管理
维度表分析:
- 属性完整性与质量
- 层级设计与实现
- 缓慢变化维度处理
- 代理键与自然键的使用
- 参考数据管理
关系网络分析:
- 星型 vs 雪花型模式
- 关系复杂度评估
- 筛选传播路径
- 交叉筛选影响评估Data Quality Assessment:
Completeness:
□ All required business entities represented
□ No missing critical relationships
□ Comprehensive attribute coverage
□ Proper handling of NULL values
Consistency:
□ Consistent data types across related columns
□ Standardized naming conventions
□ Uniform formatting and encoding
□ Consistent grain across fact tables
Accuracy:
□ Business rule implementation validation
□ Referential integrity verification
□ Data transformation accuracy
□ Calculated field correctness数据质量评估:
完整性:
□ 涵盖所有必需的业务实体
□ 无缺失的关键关系
□ 属性覆盖全面
□ 正确处理NULL值
一致性:
□ 关联列数据类型一致
□ 标准化命名规范
□ 统一的格式与编码
□ 事实表粒度统一
准确性:
□ 验证业务规则的实施
□ 参照完整性验证
□ 数据转换准确性
□ 计算字段正确性Size Optimization Assessment:
Data Reduction Opportunities:
- Unnecessary columns identification
- Redundant data elimination
- Historical data archiving needs
- Pre-aggregation possibilities
Compression Efficiency:
- Data type optimization opportunities
- High-cardinality column assessment
- Calculated column vs. measure usage
- Storage mode selection validation
Scalability Considerations:
- Growth projection accommodation
- Refresh performance requirements
- Query performance expectations
- Concurrent user capacity planning大小优化评估:
数据缩减机会:
- 识别不必要的列
- 消除冗余数据
- 历史数据归档需求
- 预聚合可能性
压缩效率:
- 数据类型优化机会
- 高基数列评估
- 计算列与度量值的使用对比
- 存储模式选择验证
可扩展性考量:
- 适应增长预测
- 刷新性能要求
- 查询性能预期
- 并发用户容量规划Performance Pattern Review:
DAX Optimization:
- Measure efficiency and complexity
- Variable usage in calculations
- Context transition optimization
- Iterator function performance
- Error handling implementation
Relationship Performance:
- Join efficiency assessment
- Cross-filtering impact analysis
- Many-to-many performance implications
- Bidirectional relationship necessity
Indexing and Aggregation:
- DirectQuery indexing requirements
- Aggregation table opportunities
- Composite model optimization
- Cache utilization strategies性能模式评审:
DAX优化:
- 度量值效率与复杂度
- 计算中变量的使用
- 上下文转换优化
- 迭代函数性能
- 错误处理实施
关系性能:
- 连接效率评估
- 交叉筛选影响分析
- 多对多关系的性能影响
- 双向关系的必要性
索引与聚合:
- DirectQuery索引要求
- 聚合表应用机会
- 复合模型优化
- 缓存利用策略Maintainability Factors:
Documentation Quality:
□ Table and column descriptions
□ Business rule documentation
□ Data source documentation
□ Relationship justification
□ Measure calculation explanations
Code Organization:
□ Logical grouping of related measures
□ Consistent naming conventions
□ Modular design principles
□ Clear separation of concerns
□ Version control considerations
Change Management:
□ Impact assessment procedures
□ Testing and validation processes
□ Deployment and rollback strategies
□ User communication plans可维护性因素:
文档质量:
□ 表与列的描述
□ 业务规则文档
□ 数据源文档
□ 关系合理性说明
□ 度量值计算解释
代码组织:
□ 相关度量值的逻辑分组
□ 一致的命名规范
□ 模块化设计原则
□ 清晰的关注点分离
□ 版本控制考量
变更管理:
□ 影响评估流程
□ 测试与验证流程
□ 部署与回滚策略
□ 用户沟通计划Security Implementation:
Row-Level Security:
□ RLS design and implementation
□ Performance impact assessment
□ Testing and validation completeness
□ Role-based access control
□ Dynamic security patterns
Data Protection:
□ Sensitive data handling
□ Compliance requirements adherence
□ Audit trail implementation
□ Data retention policies
□ Privacy protection measures安全实施:
行级别安全(RLS):
□ RLS设计与实施
□ 性能影响评估
□ 测试与验证完整性
□ 基于角色的访问控制
□ 动态安全模式
数据保护:
□ 敏感数据处理
□ 合规要求遵循
□ 审计追踪实施
□ 数据保留策略
□ 隐私保护措施Data Model Review Summary
Model Overview:
- Model name and purpose
- Business domain and scope
- Current size and complexity metrics
- Primary use cases and user groups
Key Findings:
- Critical issues requiring immediate attention
- Performance optimization opportunities
- Best practice compliance assessment
- Security and governance status
Priority Recommendations:
1. High Priority: [Critical issues impacting functionality/performance]
2. Medium Priority: [Optimization opportunities with significant benefit]
3. Low Priority: [Best practice improvements and future considerations]
Implementation Roadmap:
- Quick wins (1-2 weeks)
- Short-term improvements (1-3 months)
- Long-term strategic enhancements (3-12 months)数据模型评审摘要
模型概述:
- 模型名称与用途
- 业务领域与范围
- 当前大小与复杂度指标
- 主要使用场景与用户群体
关键发现:
- 需要立即关注的关键问题
- 性能优化机会
- 最佳实践合规性评估
- 安全与治理状态
优先级建议:
1. 高优先级: [影响功能/性能的关键问题]
2. 中优先级: [收益显著的优化机会]
3. 低优先级: [最佳实践改进与未来考量]
实施路线图:
- 快速见效项(1-2周)
- 短期改进(1-3个月)
- 长期战略增强(3-12个月)1. Table Design Analysis
□ Fact table evaluation and recommendations
□ Dimension table optimization opportunities
□ Relationship design assessment
□ Naming convention compliance
□ Data type optimization suggestions
2. Performance Architecture
□ Storage mode strategy evaluation
□ Size optimization recommendations
□ Query performance enhancement opportunities
□ Scalability assessment and planning
□ Aggregation and caching strategies
3. Best Practices Compliance
□ Star schema implementation quality
□ Industry standard adherence
□ Microsoft guidance alignment
□ Documentation completeness
□ Maintenance readiness1. 表设计分析
□ 事实表评估与建议
□ 维度表优化机会
□ 关系设计评估
□ 命名规范合规性
□ 数据类型优化建议
2. 性能架构
□ 存储模式策略评估
□ 大小优化建议
□ 查询性能提升机会
□ 可扩展性评估与规划
□ 聚合与缓存策略
3. 最佳实践合规性
□ 星型架构实施质量
□ 行业标准遵循
□ 微软指南对齐
□ 文档完整性
□ 维护就绪性For Each Issue Identified:
Issue Description:
- Clear explanation of the problem
- Impact assessment (performance, maintenance, accuracy)
- Risk level and urgency classification
Recommended Solution:
- Specific steps for resolution
- Alternative approaches when applicable
- Expected benefits and improvements
- Implementation complexity assessment
- Required resources and timeline
Implementation Guidance:
- Step-by-step instructions
- Code examples where appropriate
- Testing and validation procedures
- Rollback considerations
- Success criteria definition针对每个识别的问题:
问题描述:
- 清晰说明问题
- 影响评估(性能、维护、准确性)
- 风险等级与紧急程度分类
推荐解决方案:
- 具体解决步骤
- 适用的替代方案
- 预期收益与改进
- 实施复杂度评估
- 所需资源与时间线
实施指导:
- 分步说明
- 适用的代码示例
- 测试与验证流程
- 回滚考量
- 成功标准定义□ Model follows star schema principles
□ Appropriate storage modes selected
□ Relationships have correct cardinality
□ Foreign keys are hidden from report view
□ Date table is properly implemented
□ No circular relationships exist
□ Measure calculations use variables appropriately
□ No unnecessary calculated columns in large tables
□ Table and column names follow conventions
□ Basic documentation is present□ 模型遵循星型架构原则
□ 选择了合适的存储模式
□ 关系设置了正确的基数
□ 外键在报表视图中隐藏
□ 日期表已正确实施
□ 无循环关系
□ 度量值计算合理使用变量
□ 大型表中无不必要的计算列
□ 表与列名遵循规范
□ 具备基础文档Architecture & Design:
□ Complete schema architecture analysis
□ Detailed relationship design review
□ Storage mode strategy evaluation
□ Performance optimization assessment
□ Scalability planning review
Data Quality & Integrity:
□ Comprehensive data quality assessment
□ Referential integrity validation
□ Business rule implementation review
□ Error handling evaluation
□ Data transformation accuracy check
Performance & Optimization:
□ Query performance analysis
□ DAX optimization opportunities
□ Model size optimization review
□ Refresh performance assessment
□ Concurrent usage capacity planning
Governance & Security:
□ Security implementation review
□ Documentation quality assessment
□ Maintainability evaluation
□ Compliance requirements check
□ Change management readiness架构与设计:
□ 完整的架构分析
□ 详细的关系设计评审
□ 存储模式策略评估
□ 性能优化评估
□ 可扩展性规划评审
数据质量与完整性:
□ 全面的数据质量评估
□ 参照完整性验证
□ 业务规则实施评审
□ 错误处理评估
□ 数据转换准确性检查
性能与优化:
□ 查询性能分析
□ DAX优化机会
□ 模型大小优化评审
□ 刷新性能评估
□ 并发使用容量规划
治理与安全:
□ 安全实施评审
□ 文档质量评估
□ 可维护性评估
□ 合规要求检查
□ 变更管理就绪性Focus Areas:
- Functionality completeness
- Performance validation
- Security implementation
- User acceptance criteria
- Go-live readiness assessment
Deliverables:
- Go/No-go recommendation
- Critical issue resolution plan
- Performance benchmark validation
- User training requirements
- Post-launch monitoring plan重点领域:
- 功能完整性
- 性能验证
- 安全实施
- 用户验收标准
- 上线就绪性评估
交付物:
- 上线/不上线建议
- 关键问题解决计划
- 性能基准验证
- 用户培训需求
- 上线后监控计划Focus Areas:
- Performance bottleneck identification
- Optimization opportunity assessment
- Capacity planning validation
- Scalability improvement recommendations
- Monitoring and alerting setup
Deliverables:
- Performance improvement roadmap
- Specific optimization recommendations
- Expected performance gains quantification
- Implementation priority matrix
- Success measurement criteria重点领域:
- 性能瓶颈识别
- 优化机会评估
- 容量规划验证
- 可扩展性改进建议
- 监控与告警设置
交付物:
- 性能提升路线图
- 具体优化建议
- 预期性能收益量化
- 实施优先级矩阵
- 成功衡量标准Focus Areas:
- Current state vs. best practices gap analysis
- Technology upgrade opportunities
- Architecture improvement possibilities
- Process optimization recommendations
- Skills and training requirements
Deliverables:
- Modernization strategy and roadmap
- Cost-benefit analysis of improvements
- Risk assessment and mitigation strategies
- Implementation timeline and resource requirements
- Change management recommendations重点领域:
- 当前状态与最佳实践的差距分析
- 技术升级机会
- 架构改进可能性
- 流程优化建议
- 技能与培训需求
交付物:
- 现代化战略与路线图
- 改进的成本效益分析
- 风险评估与缓解策略
- 实施时间线与资源需求
- 变更管理建议