DMBOK2.0全知识点总结（第4-6章 数据架构 数据建模和设计 数据存储和操作）

CDMP，全称Certified for Data Management Professional，即数据管理专业人士认证，由数据管理国际协会DAMA International建立，是一项涵盖学历教育、工作经验和专业知识考试在内的综合认证。总结了CDMP英文考试的所有知识点，考点，以及历史真题。 适用于从事数据管理，数据治理，数字转型等方面的高级职业认证。章节和知识点较多，因此分章节和完成时间分发。1-3、4-6、7-9、10-12、13-17、考证 CDMP 数据管理DMBOK 数字化转型。

DAMA章节4-6知识点+真题

• Chapter 4: Data Architecture 数据架构
  • 
  • 1. Introduction
    • 1.1. ISO/IEC 42010:2007 Systems and Software Engineering – Architecture Description (2011) defines architecture as “the fundamental organization of a system, embodied in its components, their relationships to each other and the environment, and the principles governing its design and evolution.”
      • 1.1.1. 15. "According to the ISO/IEC 42010:2007 Software and Systems Engineering-Architecture Description which of the following describes the definition of architecture: A：the fundamental rules for ensuring the information captured in the architected solution is enforcing data quality and completeness B："the fundamental organization of a system, and the principles governing its design an d evolution "系统的基本组织，以及控制其设计和演化的原则” C：the fundamental collection of all artefacts that describes a system and how they work together D：the fundamental view of how the system should be built and how it will be maintained E：the fundamental responsibility for delivering the best systems at the lowest cost 正确答案：B 你的答案：A 解析：4.1：术语“架构”已经被广泛接受，并用于描述信息系统的重要设计部分。在国际标准ISO/IEC/IEEE 42010：2011中，将架构定义为“系统的基本结构，具体体现在架构构成中的组件、组件之间的相互关系以及管理其设计和演变的原则”。
      • 1.1.2. the current state of systems
      • 1.1.3. the components of a set of systems, the discipline of designing systems (architecture practice),
      • 1.1.4. the intentional design of a system or a set of systems (future state or proposed architecture),
      • 1.1.5. the artifacts that describe a system (architecture documentation)
      • 1.1.6. the team that does the design work (the Architects or the Architecture team).
    • 1.2. The discipline of Enterprise Architecture encompasses domain architectures, including business, data, application, and technology.
      • 1.2.1. 16. The key architecture domains include: A：business，strategy ,application and technology architectures. B："business, data, infrastructure and technology architectures." C："process, database, software and technology architectures." D："Zachmann, TOGAF, Cobit and Heath architectures " E："business, data, application and technology architectures." 正确答案：E 你的答案：E 解析：题解：企业架构包括多种不同类型，如包括业务架构、数据架构、应用架构和技术架构等。 Zachman框架，约翰扎科曼（John Zachman）在1987年创立的全球第一个企业架构理论，其论文《信息系统架构框架》仍被业界认为是企业架构设计方面最权威的理论。ToGAF是The Open Group Architecture Framework的缩写，它由The Open Group开发，The Open Group是一个非盈利的技术行业联盟，它不断更新和重申TOGAF。COBIT（Control Objectives for Information and related Technology）是国际上通用的信息系统审计的标准，由信息系统审计与控制协会（ISACA）在1996年公布。
    • 1.3. Well-managed enterprise architecture practices help organizations understand the current state of their systems, promote desirable change toward future state, enable regulatory compliance, and improve effectiveness.
      • 1.3.1. Effective management of data and the systems in which data is stored and used is a common goal of the breadth of architecture disciplines.
    • 1.4. Data Architecture is fundamental to data management. Because most organizations have more data than individual people can comprehend, it is necessary to represent organizational data at different levels of abstraction so that it can be understood and management can make decisions about it.
      • 1.4.1. 12. Data architecture is necessary because A：most of the data lacks documentation models and other meta-data B：much of the data that exists outside of core systems is invisible to those who govern C：enterprise data is widely dispersed 分散 and is an asset that should be leveraged D：you can govern what you do not know E：all 正确答案：C 你的答案：C 解析：4.1：数据架构是数据管理的基础。由于大多数组织拥有的数据超出了个人可以理解的范围，因此有必要在不同抽象层级上描述组织的数据，以便更好地了解数据，帮助管理层做出决策。
    • 1.5. Data Architecture artifacts 构件 includes specifications used to describe existing state, define data requirements, guide data integration, and control data assets as put forth in a data strategy. An organization’s Data Architecture is described by an integrated collection of master design documents at different levels of abstraction, including standards that govern how data is collected, stored, arranged, used, and removed.
      • 1.5.1. Data Architecture outcomes 成果 - Aritifacts 构件
      • 1.5.2. Data Architecture activities 活动 - Intentions 目标
      • 1.5.3. Data Architecture behavior 行为 - Affect 影响
      • 1.5.4. The most detailed Data Architecture design document is a formal enterprise data model, containing data names, comprehensive data 数据属性 and Metadata definitions, conceptual and logical entities 概念与逻辑实体 and relationships, and business rules. Physical data models are included, but as a product of data modeling and design, rather than Data Architecture.
      • 1.5.5. 6. Data architecture relates information needs and business functions of the enterprise through A：business requirements and information consumers B：the master set 主集 of models and requirements C：meta-data repositories and D：business projects and databases E：all 正确答案：B 你的答案：B 解析：题解：数据架构的构件包括当前状态的描述、数据需求的定义、数据整合的指引、数据管控策略中要求的数据资产管理规范。组织的数据架构是指不同抽象层级主要设计文档的集合，其中主要包括数据的收集、存储、规划、使用和删除等标准。这是按照数据的生命周期来对数据架构中包括的内容进行定义和范围界定，同时也可以按照数据在组织系统中所存储的容器和路径来进行定义和确定范围。最为详细的数据架构设计文件是正式的企业数据模型，包含数据名称、数据属性和元数据定义、概念和逻辑实体、关系以及业务规则。物理数据横型也属于数据架构文件，但物理数据模型是数据建模和设计的产物，而不是数据架构的产物。
    • 1.6. The artifacts that architects create constitute valuable Metadata. Ideally, architectural artifacts should be stored and managed in an enterprise architecture artifact repository.
    • 1.7. Forward-looking organizations should include data management professionals (e.g., Enterprise Data Architects or a strategic Data Stewards) when they are designing new market offerings, because nowadays these usually include hardware, software, and services that capture data, depend on data access, or both (新产品的设计需要数据为基础，数据架构响应了组织对新产品的配置能力）.
    • 1.8. Business Drivers
      • 1.8.1. The goal of Data Architecture is to be a bridge between business strategy and technology execution.
      • 1.8.2. As part of Enterprise Architecture, Data Architects:
        • 1. Strategically prepare organizations to quickly evolve their products, services, and data to take advantage of business opportunities inherent in emerging technologies
          • 19. The ability of an organization to respond to changes in product configuration is easier due to generalization in the A：technical architecture B：business architecture C：data architecture D：data warehousing E：data quality 正确答案：C 你的答案：C 解析：4.1.3数据架构才能响应变化
        • 2. Translate business needs into data and system requirements so that processes consistently have the data they require
        • 3. Manage complex data and information delivery throughout the enterprise
          • 5. Which of the following are typical responsibilities of an Enterprise Data Architect or Data Architect? A：Manage the day-to-day operations of a database management system B：Manage enterprise database security and privileges 权限 C：Design and implement policies and procedures for corporate data management. D：Explain standard data architecture patterns to ls project managers E：All 正确答案：C 你的答案：C 解析：4.1.2：为了达到该目的，数据架构师需要定义和维护的具体事宜如下：1）定义组织中数据的当前状态。2）提供数据和组件的标准业务词汇。3）确保数据架构和企业战略及业务架构保持一致。4）描述组织数据战略需求。5）高阶数据整合概要设计。6）整合企业数据架构蓝图。
        • 4. Facilitate alignment between Business and IT
        • 5. Act as agents for change, transformation, and agility
    • 1.9. Data Architecture Outcomes and Practices
      • 1.9.1. Outcomes 成果
        • Data storage and processing requirements
        • Designs of structures and plans that meet the current and long-term data requirements of the enterprise
      • 1.9.2. Data architects create and maintain organizational knowledge about data and the systems through which it moves. This knowledge enables an organization to manage its data as an asset and increase the value it gets from its data by identifying opportunities for data usage, cost reduction, and risk mitigation. Specifications 事项
        • 1. Define the current state of data in the organization
        • 2. Provide a standard business vocabulary for data and components
        • 3. Align Data Architecture with enterprise strategy and business architecture
        • 4. Express strategic data requirements
        • 5. Outline high-level integrated designs to meet these requirements
        • 6. Integrate with overall enterprise architecture roadmap
      • 1.9.3. Practices 实施
        • Using Data Architecture artifacts (master blueprints) to define data requirements, guide data integration, control data assets, and align data investments with business strategy
        • Collaborating with, learning from and influencing various stakeholders that are engaged with improving the business or IT systems development
        • Using Data Architecture to establish the semantics 数据语言 of an enterprise, via a common business vocabulary
    • 1.10. Essential Concepts
      • 1.10.1. Enterprise Architecture Domains 企业架构类型
        •  
        • Business
        • Data
        • Applications
        • Technology
      • 1.10.2. Enterprise Architecture Frameworks 企业架构框架
        • Enterprise Architecture Frameworks 企业架构框架 is a foundational structure used to develop a broad range of related architectures Architectural frameworks provide ways of thinking about and understanding architecture. They represent an overall ‘architecture for architecture.’
          • 1. Zachman Framework for Enterprise Architecture
            • 
            • The two dimensions in the matrix framework are the communication interrogatives (i.e., what, how, where, who, when, why) as columns and the reification transformations (Identification, Definition, Representation, Specification, Configuration, and Instantiation) as rows. The framework classifications are represented by the cells (the intersection between the interrogatives and the transformations). Each cell in the Zachman Framework represents a unique type of design artifact.
              • communication interrogatives 问询沟通维度
                • What (the inventory column): Entities used to build the architecture
                • How (the process column): Activities performed
                • Where (the distribution column): Business location and technology location
                • Who (the responsibility column): Roles and organizations
                • When (the timing column): Intervals, events, cycles, and schedules
                • Why (the motivation column): Goals, strategies, and means
              • reification transformations 重新定义转换维度
                • The executive perspective (business context): Lists of business elements defining scope inidentification models.
                • The business management perspective (business concepts): Clarification of the relationshipsbetween business concepts defined by Executive Leaders as Owners in definition models.
                • The architect perspective (business logic): System logical models detailing system requirements andunconstrained design represented by Architects as Designers in representation models.
                • The engineer perspective (business physics): Physical models optimizing the design forimplementation for specific use under the constraints of specific technology, people, costs, andtimeframes specified by Engineers as Builders in specification models.
                • The technician perspective (component assemblies): A technology-specific, out-of-context view ofhow components are assembled and operate configured by Technicians as Implementers inconfiguration models.
                • The user perspective (operations classes): Actual functioning instances used by Workers asParticipants. There are no models in this perspective.
          • 2. Togaf 框架
          • 3. FEA 框架等
        • ISO/IEC/IEEE 42010:2011, Systems and software engineering — Architecture description and a comparison table.32 Common frameworks and methods include Data Architecture as one of the architectural domains.
      • 1.10.3. Enterprise Data Architecture 企业数据架构
        • Enterprise Data Model (EDM) 企业数据模型
          • The EDM is a holistic 整体的, enterprise-level, implementation-independent conceptual or logical data model providing a common consistent view of data across the enterprise. An EDM includes key enterprise data entities (i.e., business concepts),their relationships, critical guiding business rules, and some critical attributes. It sets forth thefoundation for all data and data-related projects.
            • Adopting an industry standard model 行业标准模型 can jump start the process of developing an EDM.
            • 1. Which of the following is a difference between an enterprise data model and a logical data model? A：An enterprise data model presents the entities of an organization without respect to their relationships. the logical model takes into consideration the relationships B：An enterprise data model represents the business rules of an organization the logical model describes how the data entities will be structured in enterprise database technology C：An enterprise data model demonstrates how the entities can be translated into the enterprise database. The logical model presents the business rules of an organization D：An enterprise data model represents the entities and relationships of an organization. The logical model represents a model that can be presented to a database. E：all 正确答案：D 你的答案：D 解析：4.1.3：1）企业数据模型。企业数据模型是一个整体的、企业级的、独立实施的概念或逻辑数据模型，为企业提供通用的、一致的数据视图。通常用于表示高层级简化的数据模型，也表示了不同抽象层级。企业数据模型包括数据实体（如业务概念）、数据实体间关系、关键业务规则和一些关键属性，它为所有数据和数据相关的项目奠定了基础。（3）物理数据模型物理数据模型（Physical Data Model，PDM）描述了一种详细的技术解决方案，通常以逻辑数据模型为基础，与某一类系统硬件、软件和网络工具相匹配。
            • Figure 23 shows how different types of models are related and how conceptual models are ultimately linkable to physical application data models. It distinguishes:
              • 
              • 1. A conceptual overview over the enterprise’s subject areas
              • 2. Views of entities and relationships for each subject area
              • 3. Detailed, partially attributed logical views of these same subject areas
              • 4. Logical and physical models specific to an application or project
              • 5. 21. An enterprise data model would be composed of: A：conceptual models，logical models and physical models B：conceptual models star schema models and interface models. C：logical models，physical models and infrastructure models. D：enterprise models，data models and compositional models. E：conceptual models，subject area models and logical models. 正确答案：E 你的答案：A 解析：正确答案：E来源：4.1.3题解： 1）企业数据模型。企业数据模型是一个整体的、企业级的、独立实施的概念或逻辑数据模型，为企业提供通用的、一致的数据视图。通常用于表示高层级简化的数据模型，也表示了不同抽象层级。企业数据模型包括数据实体（如业务概念）、数据实体间关系、关键业务规则和一些关键属性，它为所有数据和数据相关的项目奠定了基础。任何项目级的数据模型必须基于企业数据模型设计。企业数据模型应该由利益相关方审核，以便它能一致有效地代表企业。
            • All levels are part of the Enterprise Data Model, and linkages create paths to trace an entity from top to bottom and between models in the same level.
              • Vertical: mapping 映射
              • Horizontal: related 关联
            • Subject Area Models Diagram Example
              • 
              • the conceptual enterprise data model is built up by the combination of Subject Area models. The enterprise data model can be built using a top-down approach or using a bottom-up approach. A combination of the approaches is usually recommended;
                • The top-down approach means starting with forming the Subject Areas and then populating them with models.
                • When using a bottom-up approach the Subject Area structure is based on existing data models.
                • starting with bottom-up using existing models and completing the enterprise data model by populating the models by delegating Subject Area modeling to projects.
              • The Subject Area structure is usually most effective for Data Architecture work if it is formed using normalization rules. The normalization process will establish the major entities that carry/constitute each Subject Area
                • 4. In data modeling subject areas are useful for A：grouping data attributes by data type B：organizing a project into phases C：organizing a management presentation on your application D：grouping entities to make model management easier. E：none 正确答案：D 你的答案：D 解析：4.1.3：因此，企业概念数据模型是由主题域模型相结合构建的。每个企业数据模型既可以采用自上而下，也可以采用自下而上的方法进行构建。自上而下是从主题域开始，先设计主题，再逐步设计下层模型。而采用自下而上的方法时，主题域结构则是基于现有逻辑数据模型向上提炼抽象而成。通常推荐两种方法相结合，即自下而上地从分析现有模型开始，自上而下地设计主题模型，通过两种方法的结合来共同完成企业数据模型的设计工作。
        • Data Flow Design 数据流设计
          • Defines the requirements and master blueprint for storage and processing across databases, applications, platforms, and networks (the components). These data flows map the movement of data to business processes, locations, business roles, and to technical components.
          • Data flows are a type of data lineage documentation that depicts how data moves through business processes and systems. End-to-end data flows illustrate where the data originated, where it is stored and used, and how it is transformed as it moves inside and between diverse processes and systems. Data lineage analysis can help explain the state of data at a given point in the data flow.
          • Data flows map and document relationships between data and
            • 1. Applications within a business process
            • 2. Data stores or databases in an environment
            • 3. Network segments (useful for security mapping)
            • 4. Business roles, depicting 描述 which roles have responsibility for creating, updating, using, and deleting data (CRUD增删改查)
            • 5. Locations where local differences occur
            • 6. 2. which of the following will NOT be included in a process model? A：Flow of control B：Data flows C：External agents D：Subject areas E：all 正确答案：D 你的答案：C 解析：正确答案：D来源：4.1.3题解：4.1.3数据流可以用于描述不同层级模型的映射关系：主题域、业务实体，乃至属性层面的映射关系。系统可以通过网络、平台、常用应用集或独立服务器呈现。数据流可以通过二维矩阵（图4-5）或数据流图（图4-6）的方式呈现。右图为数据流图实例
          • Data Flow Depicted in a Matrix 二维矩阵图
            • 
            • A matrix gives a clear overview of what data the processes create and use.
            • The benefits of showing the data requirements in a matrix is that it takes into consideration that data does not flow in only one direction; the data exchange between processes are many-to-many in a quite complex way, where any data may appear anywhere. In addition, a matrix can be used to clarify the processes’ data acquisition responsibilities 数据获取职责 and the data dependencies between the processes 数据依赖关系, which in turn improves the process documentation.
          • Data Flow Diagram Example
            • 
            • The data flow in Figure 26 is a traditional high-level data flow diagram depicting what kind of data flows between systems. Such diagrams can be described in many formats and detail levels.
            • 7. In a data flow diagram a data store is used to indicate A：data archived for audit support B：a time delay between processes using the same data C：data coming into the system from outside sources D：backup data supporting a checkpoint operation E：None 正确答案：B 你的答案：C 解析：使用相同数据的进程之间的时间延迟，AD对，但是太窄了。C不一定，每进入一次data store，则延迟一次。
            • 13. ln a data flow diagram data should not flow from A：an external agent to a data store B：a process to an external agent. C：a data store to a process. D：a process to a data store E：none 正确答案：A 你的答案：B 解析：外部数据不直接到存储，要先经过process，才能进存储。External Entity-Also known as actors, sources or sinks, and terminators, external entities produce and consume data that flows between the entity and the system being diagrammed. These data flows are the inputs and outputs of the DFD. Since they are external to the system being analyzed, these entities are typically placed at the boundaries of the diagram. They can represent another system or indicate a subsystem.
        • They need to be reflected in current state and target state 当前和目标态 (architecture perspective 架构视角), and also in transition state 过渡状态 (project perspective 项目视角).
  • 2. Activities
    • 2.1. Data and enterprise architecture deal with complexity from two viewpoints:
      • 2.1.1. Quality-oriented 面向质量的传统设计
        • Focus on improving execution within business and IT development cycles.
        • aligns with traditional Data Architecture work where architectural quality improvements are accomplished incrementally
      • 2.1.2. Innovation-oriented 面向创新的针对性设计
        • Focus on transforming business and IT to address new expectations and opportunities.
        • The innovation-oriented approach can have a shorterterm perspective and be using unproven business logic and leading edge technologies.
    • 2.2. Establish Data Architecture Practice
      • 2.2.1. Includes the following work streams, executed serially or in parallel 串行或并行执行
        • Strategy: Select frameworks, state approaches, develop roadmap
        • Acceptance and culture: Inform and motivate changes in behavior
        • Organization: Organize Data Architecture work by assigning accountabilities and responsibilities
        • Working methods: Define best practices and perform Data Architecture work within development projects, in coordination with Enterprise Architecture
        • Results: Produce Data Architecture artifacts within an overall roadmap
      • 2.2.2. Scopes
        • 1. Defining project data requirements
          • Data Architects provide enterprise data requirements for individual projects.
        • 2. Reviewing project data designs
          • Design reviews ensure that conceptual, logical, and physical datamodels are consistent with architecture and in support of long-term organizational strategy.
        • 3. Determining data lineage impact 溯源影响
          • Ensures that business rules in the applications along the data floware consistent and traceable.
        • 4. Data replication control 数据复制控制
          • Replication is a common way to improve application performance and makedata more readily available, but it can also create inconsistencies in the data.
        • 5. Enforcing Data Architecture standards
          • Formulating and enforcing standards for the EnterpriseData Architecture lifecycle.
        • 6. Guide data technology and renewal decisions 更新决策
          • The Data Architect works with Enterprise Architectsto manage data technology versions, patches, and policies each application uses, as a roadmap for datatechnology.
        • 7. 20. which of the following activities is not a way that enterprise data architecture influences the scope boundaries of projects A：ensuring sufficient data replication controls are in place 副本 B：providing enterprise data requirement for projects C：ensuring enterprise business processes are effectively documented 被记录 D：enforcing data architecture standards E：performing design reviews to ensure support of long-term organizational strategy. 正确答案：C 你的答案：A 解析：文档存储不影响范围和边界，A副本说明由更多的应用范围
      • 2.2.3. Steps
        • 1. Evaluate Existing Data Architecture Specifications 规范
          • Every organization has some form of documentation for its existing systems. Identify these documents and evaluate them for accuracy, completeness, and level of detail. If necessary, update them to reflect the current state.
        • 2. Develop a Roadmap
          • Even in an ideal situation, data dependencies would quickly arise and need to be managed. A roadmap provides a means to manage these dependencies and make forward-looking decisions. A roadmap helps an organization see trade-offs and formulate a pragmatic plan, aligned with business needs and opportunities, external requirements, and available resources.
          • A roadmap for Enterprise Data Architecture describes the architecture’s 3-5 year development path. Together with the business requirements, consideration of actual conditions, and technical assessments, the roadmap describes how the target architecture will become reality
          • The Enterprise Data Architecture roadmap must be integrated into an overall enterprise architecture roadmap that includes high-level milestones, resources needed, and costs estimations, divided in business capability work streams.
            • 3. Which of the following BEST characterizes a planning-level data architecture? A：Data models contained in both corporate and departmental database B：Master data designs that are shared among operating units C：All designs contained in existing corporate databases D：a high-level view of all data used by the enterprise E：none 正确答案：D 你的答案：D 解析：暂无解析
          • A business-data-driven roadmap starts with the business capabilities that are most independent, then resolve each dependency in steps from top to bottom.
            • 
        • 3. Manage Enterprise Requirements within Projects
          • Architecture should not be locked into the limitations that prevail at the time it is developed.
          • Development projects implement solutions for capturing, storing, and distributing data based on business requirements and the standards established by the Enterprise Data Architecture.
          • At the project level, the process of specifying requirements via a data model begins with review of business needs.
          • Importantly, data architects must be able to understand requirements in relation to the overall architecture. When a project specification is completed, the data architects should determine:
            • 1. Whether enterprise-wide entities 实体 represented in the specification 规范中 conform to 符合 agreed-upon standards
            • 2. What entities in the requirements specification should be included in the overall Enterprise Data Architecture
            • 3. Whether entities and definitions in this specification need to be generalized or improved 扩大或加深 upon to handle future trends
            • 4. Whether new data delivery architectures are indicated or whether to point the developers in the direction of reuse
          • Project-related activities include
            • Define scope
              • Ensure the scope and interface are aligned with the enterprise data model
            • Understand business requirements
              • Capture data-related requirements such as entity, source(s),availability, quality, and pain points, and estimate the business value of meeting these requirements
            • Design
              • Form detailed target specifications, including business rules in a data lifecycle perspective.
            • Implement
              • When buying: reverse engineer 逆向工程 purchased applications (Commercial Off the Shelf – COTS商用现成品) and map against data structure, data models
              • When reusing data
              • When building
          • Development methodology
            • Waterfall methods
              • Understand the requirements and construct systems in sequential phases as part ofan overall enterprise design.
            • Incremental methods
              • Learn and construct in gradual steps (i.e., mini-waterfalls). This method createsprototypes based on vague overall requirements.
            • Agile, iterative, methods
              • Scrum
              • DevOps
    • 2.3. Integrate with Enterprise Architecture 整合其他企业架构
      • 2.3.1. to integrate Enterprise Data Architecture matters with project portfolio management. Doing so enables implementation of the roadmap and contributes to better project outcomes.
  • 3. Tools
    • 3.1. Data Modeling Tools 数据建模工具
      • 3.1.1. Data modeling tools and model repositories are necessary for managing the enterprise data model in all levels.
    • 3.2. Asset Management Software 资产管理软件
      • 3.2.1. Asset management software is used to inventory systems, describe their content, and track the relationships between them.
    • 3.3. Graphical Design Applications 图形设计应用
      • 3.3.1. Graphical design applications are used to create architectural design diagrams, data flows, data value chains, and other architectural artifacts.
  • 4. Techniques
    • 4.1. Lifecycle Projections 生命周期预测
      • 4.1.1. Current 当前的: Products currently supported and used
      • 4.1.2. Deployment period 部署周期的: Products deployed for use in the next 1-2 years
      • 4.1.3. Strategic period 策略周期的: Products expected to be available for use in the next 2+ years
      • 4.1.4. Retirement 退役的: Products the organization has retired or intends to retire within a year
      • 4.1.5. Preferred 优先的: Products preferred for use by most applications
      • 4.1.6. Containment 限制的: Products limited to use by certain applications
      • 4.1.7. Emerging 新兴的: Products being researched and piloted for possible future deployment
      • 4.1.8. Reviewed 审核的: Products that have been evaluated, the evaluation results and are currently not in any other status above
    • 4.2. Diagramming Clarity 图标使用规范
      • 4.2.1. A clear and consistent legend 说明
      • 4.2.2. A match between all diagram objects and the legend
      • 4.2.3. A clear and consistent line direction
      • 4.2.4. A consistent line cross display method
      • 4.2.5. Consistent object attributes 对象属性
      • 4.2.6. Linear symmetry 线性对称
  • 5. Implementation Guidelines
    • 5.1. the Enterprise Data Architecture will evolve incrementally. However, this agile approach needs to ensure that data architects are engaged early in development initiatives, as these evolve rapidly in an inventive culture.
      • 5.1.1. Organizing the Enterprise Data Architecture teams and forums
      • 5.1.2. Producing the initial versions of Data Architecture artifacts, such as enterprise data model, enterprisewide data flow and road maps
      • 5.1.3. Forming and establishing a data architectural way of working in development projects
      • 5.1.4. Creating awareness throughout the organization of the value of Data Architecture efforts
    • 5.2. Readiness Assessment / Risk Assessment Include
      • 5.2.1. Lack of management support
      • 5.2.2. No proven record of accomplishment 缺少成功的证据
      • 5.2.3. Apprehensive sponsor 缺乏管理者新人
      • 5.2.4. Counter-productive executive decisions 不正确的管理决策
      • 5.2.5. Culture shock
      • 5.2.6. Inexperienced project leader
      • 5.2.7. Dominance of a one-dimensional view 单一维度视角
    • 5.3. Organization and Cultural Change Rely on
      • 5.3.1. Cultural receptivity 接受度 to architectural approach (developing an architecture-friendly culture)
      • 5.3.2. Organizational recognition of data as a business asset, not just an IT concern
      • 5.3.3. Organizational ability to let go of a local perspective 放弃局部视角 and adopt an enterprise perspective on data
      • 5.3.4. Organizational ability to integrate architectural deliverables into project methodology 项目实施能力
      • 5.3.5. Level of acceptance 接受度 of formal data governance
      • 5.3.6. Ability to look holistically 全局 at the enterprise, rather than being focused solely on project delivery and IT solutioning (Edvinsson, 2013)
  • 6. Data Architecture Governance
    • 6.1. Enterprise Data Architecture and the Data Governance organization have to be well aligned. Ideally, both a data architect and a Data Steward should be assigned to each subject area and even to each entity within a subject area. In addition, business oversight should be aligned to process oversight. Business event subject areas should be aligned with business process governance as each event entity usually corresponds to a business process.
    • 6.2. Activities
      • 6.2.1. Overseeing Projects 项目监督
        • This includes ensuring that projects comply with required Data Architectureactivities, use and improve architectural assets, and implement according to stated architecturalstandards.
      • 6.2.2. Managing architectural designs, lifecycle, and tools
        • Architectural designs must be defined,evaluated and maintained. Enterprise Data Architecture serves as a ‘zoning plan’ for long-termintegration. Future state architecture affects project objectives and influences the priority of theprojects in the project portfolio.
      • 6.2.3. Defining standards
        • Setting the rules, guidelines, and specifications for how data is used within theorganization.
      • 6.2.4. Creating data-related artifacts
        • Artifacts that enable compliance with governance directives
    • 6.3. Metrics
      • 6.3.1. Architecture standard compliance rate 架构标准接受率
        • measures how closely projects comply with established Data Architectures and how well projects adhere to processes for engaging with enterprise architecture.
      • 6.3.2. Implementation trends 实施趋势
        • Use/reuse/replace/retire measurements
        • Project execution efficiency measurements
      • 6.3.3. Business value measurements 业务价值度量指标
        • Business agility improvements
        • Business quality
        • Business operation quality
        • Business environment improvements
  • 7. Works Cited / Recommended
    • 7.1. 8. A business rule contains all of the following characteristics EXCEPT that it A：is applied toward business action (s) B：represents a business service C：is complete and specific. D：supports current policies. E：None 正确答案：B 你的答案：B 解析：4.1.3规则不是服务 1）企业数据模型。企业数据模型是一个整体的、企业级的、独立实施的概念或逻辑数据模型，为企业提供通用的、一致的数据视图。通常用于表示高层级简化的数据模型，也表示了不同抽象层级。企业数据模型包括数据实体（如业务概念）、数据实体间关系、关键业务规则和一些关键属性
    • 7.2. 9. Specialized hardware used to support data requirements include A：data appliances 数据设备 B：data warehouses C：repositories 资料库 D：capacitors 电容器 E：AIl 正确答案：A 你的答案：E 解析：A是硬件，BC是软件，D无关，4.1.具有前瞻性的组织在设计新产品时，设计团体应该包括数据管理专业人员（如企业数据架构师或战略数据管理员），因为现在新产品的设计需要以数据为基础，而数据通常需要涉及捕获数据的硬件、软件和服务以及依赖数据访问服务等。
    • 7.3. 10. Systems re-engineering includes each of the following EXCEPT A：enhancing the architecture of legacy systems B：developing an enterprise information policy. C：reverse engineering from physical to logical D：forward engineering from logical to physical E：None 正确答案：B 你的答案：D 解析：B制定企业信息策略不属于工程化活动。相关章节：4.2.1组织经常要等到项目需要重新设计数据存储和集成的时候，才来解决数据架构问题。但是，最好在规划的早期和整个项目生命周期中考虑这些因素。
    • 7.4. 11. Goal setting, measurement communications facilitation, consensus building and monitoring of data assets are part of the skill set that is required of a A：project lead B：data analyst. C：data steward D：data owner. E：none 正确答案：C 你的答案：C 解析：正确答案：C来源：3.1.3题解：其他选项职责与此不符管理专员（Steward，直译为管家，本书译为管理专员）指其职责是为别人管理财产的人。数据管理专员代表他人的利益并为组织的最佳利益来管理数据资产（McGilvray，2008）。数据管理专员代表所有相关方的利益，必须从企业的角度来确保企业数据的高质量和有效使用。有效的数据管理专员对数据治理活动负责，并有部分时间专门从事这些活动。
    • 7.5. 14. A data architecture team is best described as A：an operational data provisioning group B：the authors of reference data C：a strategic planning and compliance team D：a group of strong database administrators. E：a well-managed project of architectural development. 正确答案：C 你的答案：C 解析：E说的是项目不合理。C合理，架构设计涉及考虑策略，计划目标和合规约束的权衡。
    • 7.6. 17. A data architect is best deployed: 部署 A：to manage the delivery of all the data aspects of a program B：after the project completes to identify weaknesses and lessons learned C：to build the database solution D：by project managers to sign off all data deliverables. E：during the early stages of a project to define and shape a strategic solution 正确答案：E 你的答案：E 解析：4.2.1：组织经常要等到项目需要重新设计数据存储和集成的时候，才来解决数据架构问题。但是，最好在规划的早期和整个项目生命周期中考虑这些因素。
    • 7.7. 18. The repeated implementation of different CRM technologies with different data structures is mostly a failure of: A：data architecture B：data quality. C：data security D：data warehousing E：data modelling 正确答案：A 你的答案：A 解析：：数据架构问题。如果是建模问题，可以调整数据模型
    • 7.8. 22. when a project specification is reviewed by the enterprise data architect，which of the following is not a consideration. A：whether enterprise-wide entities conform to agreed standards B：whether entities and definitions need to be generalized or improved to handle future trends C：what entities in the requirements should be included back into the overall enterprise data architecture D：whether to reuse existing, or develop new data delivery architectures E：whether entities on individual screens 各个屏幕and reports align with the database 正确答案：E 你的答案：E 解析：E太具体和细节，不是架构师的关注点
    • 7.9. 23. which of these are NOT true of data governance? A：DG is the exercise of authority and control over the management of data assets B：IT is a key stakeholder in DG C：DG is a continuous process of data improvement D：There are different organization models for dg E：A DG initiative should always be led by the lT department 正确答案：E 你的答案：E 解析：数据治理委员会或CDO等，不一定非要是IT部分
    • 7.10. 24. Following the rollout of a data issue process there have been no issues recorded in the first month The reason for this might be A：Lack of credibility in the data governance process to affect changes B：Staff staying back late to enter the issues into the system C：the automatic deletion of all issues in the database. D：There are no data issues in the enterprise. E：The denial of overtime requests 正确答案：A 你的答案：A 解析：没有记录，说明缺乏反映变更记录的过程
    • 7.11. 25. An effective data governance communication program should include the following: A：custom training program B：All answers C：Regular newsletters D：Events that encourage informal networking E：A Data governance portal 正确答案：B 你的答案：B 解析：全都包含
• Chapter 5: Data Modeling and Design 数据建模与设计
  • 
  • 1. Introduction
    • 1.1. Definition
      • 1.1.1. Data modeling is the process of discovering, analyzing, and scoping data requirements, and then representing and communicating these data requirements in a precise form called the data model. This process is iterative and may include a conceptual, logical, and physical model.
      • 1.1.2. The modeling process requires that organizations discover and document how their data fits together 数据组合的方式.
      • 1.1.3. Schemes
        • 1. Relational 关系模式
        • 2. Dimensional 多维模式
        • 3. Object-Oriented 面向对象模式
        • 4. Fact-Based 事实模式
        • 5. Time-Based 实践序列模式
        • 6. NoSQL 泛指非关系模式
      • 1.1.4. Three levels of detail
        • 1. Conceptual 概念模型
        • 2. Logical 逻辑模型
        • 3. Physical 物理模型
      • 1.1.5. Components
        • Entities 实体
        • Relationships 关系
        • facts 事实
        • keys 键
        • attributes 属性
    • 1.2. Business Drivers
      • 1.2.1. Provide a common vocabulary around data
        • 20. Data Models are critical to effective management of data because they A：provide a common vocabulary around data B：strategically prepare organizations to quickly evolve their products and services C：control the list of values in dropdown lists in applications D：determine which style of data schema is used in a data warehouse. E：define the rules and approval mechanisms to make changes to the data structures 正确答案：A 你的答案：E 解析：5.1.1：5.1.1业务驱动因素 数据模型对于有效的数据管理至关重要，如：1）提供有关数据的通用词汇表。2）获取、记录组织内数据和系统的详细信息。3）在项目中作为主要的交流沟通工具。4）提供了应用定制、整合，甚至替换的起点。
      • 1.2.2. Capture and document explicit 详细的 knowledge about an organization’s data and systems
      • 1.2.3. Serve as a primary communications tool during projects
      • 1.2.4. Provide the starting point for customization, integration, or even replacement of an application
    • 1.3. Goals and Principles
      • 1.3.1. Goals
        • To confirm and document understanding of different perspectives, which leads to applications that more closely align with current and future business requirements, and creates a foundation to successfully complete broad-scoped initiatives such as Master Data Management and data governance programs.
        • Proper data modeling leads to lower support costs and increases the reusability opportunities for future initiatives, thereby reducing the costs of building new applications.
      • 1.3.2. Principle
        • 1. Formalization 格式化
          • a data model (a concise 简洁 definition of data structures and relationships) makes data easier to consume
        • 2. Scope definition
          • A data model can help explain the boundaries for data context and implementationof purchased application packages, projects, initiatives, or existing systems.
        • 3. Knowledge retention/documentation
          • It serves as documentation for future projects to use as the as-is version 原始记录/实例版本.
          • Data models help us understand an organization or business area,an existing application, or the impact of modifying an existing data structure.
          • the modeler enables others to understand an information landscape 蓝图 (Hoberman, 2009)
    • 1.4. Essential Concepts
      • 1.4.1. Data Modeling and Data Models 数据建模和数据模型
        • Data modeling is most frequently performed in the context of systems development and maintenance efforts, known as the system development lifecycle (SDLC).
        • Data modeling can also be performed for broad-scoped initiatives (e.g., Business and Data Architecture, Master Data Management, and data governance initiatives) where the immediate end result is not a database but an understanding of organizational data.
        • A model is a representation of something that exists or a pattern for something to be made. A model can contain one or more diagrams.
        • Model diagrams make use of standard symbols that allow one to understand content. Maps, organization charts, and building blueprints are examples of models in use every day.
        • Data models are the main medium used to communicate data requirements from business to IT and within IT from analysts, modelers, and architects, to database designers and developers.
      • 1.4.2. Types of Data that are Modeled 建模的数据类型
        • 1. Category information 类别信息
          • Data used to classify and assign types to things. For example, customersclassified by market categories or business sectors; products classified by color, model, size, etc.;orders classified by whether they are open or closed.
        • 2. Resource information 资源信息 - Reference Data 参考数据
          • Basic profiles of resources needed conduct operational processes such asProduct, Customer, Supplier, Facility, Organization, and Account. Among IT professionals,
        • 3. Business event information 业务事件信息 - Transactional business data 交易性业务数据
          • Data created while operational processes are in progress. Examplesinclude Customer Orders, Supplier Invoices, Cash Withdrawal, and Business Meetings. Among ITprofessionals, event entities are sometimes referred to as transactional business data.
        • 4. Detail transaction information 详细交易信息 - Big Data 大数据
          • Detailed transaction information is often produced through point-of-sale systems (either in stores or online). It is also produced through social media systems, otherInternet interactions (clickstream, etc.), and by sensors in machines
          • This type of detailedinformation can be aggregated, used to derive other data, and analyzed for trends, similar to how thebusiness information events are used. This type of data (large volume and/or rapidly changing) is usually referred to as Big Data.
            • 1. Derived data 衍生数据 is redundant 冗余 because it A：is only calculated data B：is a backup for the primitive data C：has the same name as primitive data D：is transformed primitive data 原始数据转换而来 E：all 正确答案：D 你的答案：B 解析：正确答案：D来源：5.1.3题解：题干解释.4）详细交易信息（Detail Transaction Information）。详细的交易信息通常通过销售系统（商店或在线应用）生成。它还可以通过社交媒体系统、其他互联网交互（单〈双〉击流等）和机器上的传感器产生。这些传感器可以是船只和车辆的部件、工业组件或个人设备（全球定位系统、射频识别、无线等）。这种类型的详细信息可以被聚合，用于派生其他数据，并用以分析趋势，类似于业务时间信息的使用方式。这种类型的数据（大容量或快速变化）通常被称为大数据。
      • 1.4.3. Data Model Components 数据模型的组件
        • 1. Entities 实体
          • Outside of data modeling, the definition of entity is a thing that exists separate from other things. Within data modeling, an entity is a thing about which an organization collects information.
          • Commonly Used Entity Categories 5W-H-M
            • Who
            • What
            • When
            • Where
              • Mailing Address, Distribution Point, Website URL, IP Address
            • Why
            • How
            • Measurement
          • Entity Aliases 实体的别名
            • Entity type as Entity, normal entity as Entity Instance 实体实例
            • Class 类 or Object 对象 in object-oriented schemes
            • hub 中心 satellite 卫星 and link 链接 in time-based schemes
            • document 文件 or node 节点 in NoSQL schemes
            • concept or term at the conceptual level
            • table at the physical level
          • Graphic Representation of Entities 实体的图形表示
            • rectangles 矩形 independent 独立实体，一表
            • rectangles with rounded edges 矩形带圆边 dependent entities 非独立实体，多表外键
          • Definition of Entities 实体的定义
            • Entity definitions are essential contributors to the business value of any data model. They are core Metadata. High quality definitions clarify the meaning of business vocabulary and provide rigor to the business rules governing entity relationships.
              • 24. Common understanding of the core business concepts and terminology is the objective of which deliverable? A：Business Glossary 业务词汇表 B：Data Dictionary C：Data Warehouse architecture D：security Framework E：Metadata Repositories 元数据仓库 正确答案：A 你的答案：E 解析：5.1.3：3）实体的定义。实体的定义对于任何数据横型所描述的业务价值都有巨大贡献。它们属于核心元数据。高质量的定义澄清了业务词汇表的含义，并有助于精确管理实体之间关系所描述的业务规则。它们帮助业务和IT专业人员针对业务和应用程序设计做出明确的决策。高质量的数据定义具备以下3个基本特征：
            • High quality data definitions exhibit three essential characteristics:
              • 1. Clarity
                • The definition should be easy to read and grasp. Simple, well-written sentences withoutobscure acronyms or unexplained ambiguous terms such as sometimes or normally.
              • 2. Accuracy
                • The definition is a precise and correct description of the entity. Definitions should bereviewed by experts in the relevant business areas to ensure that they are accurate.
              • 3. Completeness 完整
                • All of the parts of the definition are present. For example, in defining a code, examplesof the code values are included. In defining an identifier, the scope of uniqueness in included in thedefinition.
          • 4. Which of the following would NOT require that an entity be included in a data model A：This entity appears often in the business policy documents provided by the user. B：The analyst feels that the business will expand to need this entity C：The user anticipates a need for attributes associated with this entity D：The entity has been included in earlier iterations of the model E：none 正确答案：B 你的答案：D 解析：因为B目前没有数据，未来才有，只是分析师看法；C是有客户需求了，已经有了。
        • 2. Relationships 关系
          • Relationship Aliases 关系的别名
            • navigation path 导航路径 in dimensional schemes
            • edge or link 边界 链接 in NoSQL
            • A relationship captures the high-level interactions 高级别交互 between conceptual entities, the detailed interactions 详细交互 between logical entities, and the constraints 约束 between physical entities.
          • Graphic Representation of Relationships 关系的图形表示
            • Relationships are shown as lines on the data modeling diagram.
            • The symbols on the line (called cardinality 基数) capture the rules in a precise syntax.
          • Relationship Cardinality 关系的基数
            • In a relationship between two entities, cardinality captures how many of one entity (entity instances 实体实例) participates in the relationship with how many of the other entity.
            • zero, one, or many
          • Arity of Relationships 关系的元数
            • Unary Relationship 一元关系 (Recursive 递归、self-referencing 自我引用)
              • only one entity
              • A one-to-many recursive relationship describes a hierarchy
              • A many-to-many relationship describes a network or graph
            • Binary Relationship 二元关系
              • involves two entities
            • Ternary Relationship 三元关系
              • includes three entities. An example in fact-based modeling 基于事实建模(object-role notation 对象角色表示法) appears in Figure 35
              • 12. what type of relationship is similar to a facet taxonomy 面分类in content management? A：Ternary 三元的 B：Parent-child 亲子 C：Optional 可选 D：Binary 二项 E：Recursive 递归 正确答案：A 你的答案：A 解析：5.1.3.：4）面分类法（Facet Taxonomy）。它指的是每个节点与中心节点相关联，其形状看起来像星形图。每个面是中心对象的一个属性。这里的例子是元数据，其中每个属性（创建者、标题、访问权限、关键字、版本等）是内容对象的一个面。
          • Foreign Key 外键
            • A foreign key is used in physical and sometimes logical relational data modeling schemes to represent a relationship.
            • Foreign keys appear in the entity on the many side 多的一边 of the relationship, often called the child entity 子实体.
        • 3. Attributes 属性
          • An attribute 属性 is a property that identifies, describes, or measures an entity. Attributes may have domains, which will be discussed in Section 1.3.3.4. The physical correspondent of an attribute in an entity is a column, field, tag, or node in a table, view, document, graph, or file.
          • Graphic Representation of Attributes 属性的图形表示
            • attributes are generally depicted 描述 as a list within the entity rectangle
          • Identifiers 标识符
            • An identifier (also called a key) is a set of one or more attributes that uniquely defines an instance of an entity.
              • Construction-type Keys 键的结构类型
                • Simple key 单一键
                  • simple key is one attribute that uniquely identifies an entity instance. Universal Product Codes (UPCs) and Vehicle Identification Numbers (VINs) are examples of simple keys.
                  • Universal Product Codes (UPCs) 通用产品代码 and Vehicle Identification Numbers (VINs) 车辆识别号
                  • Surrogate key 代理键 is a unique identifier. 如自增序号
                    • A surrogate key is also an example of a simple key. A surrogate key is a unique identifier for a table.
                • Compound key 组合键 two or more attributes
                  • A compound key is a set of two or more attributes that together uniquely identify an entity instance.
                • Composite key 复合键 one other simple or compound key or non-key attribute
                  • composite key contains one compound key and at least one other simple or compound key or non-key attribute.
              • Function-type Keys 键的功能类型
                • super key 超键
                  • A super key is any set of attributes that uniquely identify an entity instance.
                • candidate key 候选键
                  • A candidate key is a minimal set of one or more attributes (i.e., a simple or compound key) that identifies the entity instance to which it belongs.
                • business key 业务键
                  • A business key is one or more attributes that a business professional would use to retrieve a single entity instance. Business keys and surrogate keys are mutually exclusive.
                  • natural keys 自然键
                    • Candidate keys can be business keys (sometimes called natural keys).
                • primary key 主键
                  • A primary key is the candidate key that is chosen to be the unique identifier for an entity. Even though an entity may contain more than one candidate key, only one candidate key can serve as the primary key for an entity.
                • alternate key 备用件
                  • An alternate key is a candidate key that although unique, was not chosen as the primary key. An alternate key can still be used to find specific entity instances.
                • Often the primary key is a surrogate key and the alternate keys are business keys.
            • Identifying vs. Non-Identifying Relationships 标识关系与非标识关系
              • An independent entity is one where the primary key contains only attributes that belong to that entity.
              • A dependent entity is one where the primary key contains at least one attribute from another entity.
              • In relational schemes, most notations depict independent entities on the data modeling diagram as rectangles 直角矩形 and dependent entities as rectangles with rounded corners 圆角矩形.
              • Dependent entities have at least one identifying relationship. An identifying relationship is one where the primary key of the parent (the entity on the one side of the relationship) is migrated as a foreign key to the child’s primary key, as can be seen with the relationship from Student to Registration, and from Course to Registration. In non-identifying relationships, the primary key of the parent is migrated as a non-primary foreign key attribute to the child.
        • 4. Domains 域
          • In data modeling, a domain is the complete set of possible values that an attribute can be assigned. A domain may be articulated in different ways (see points at the end of this section). A domain provides a means of standardizing the characteristics of the attributes.
          • All values inside the domain are valid values. Those outside the domain are referred to as invalid values.
          • ways
            • Data Type 数据类型: Integer, Character(30), and Date
            • Data Format 数据格式: alphanumeric only, alphanumeric with certain special characters allowed, etc
            • List 列表: dropdown lists 下拉菜单
            • Range 范围: minimum and/or maximum values
              • Domains that allow all values of the same data type that are between one or more minimumand/or maximum values.
            • Rule-based 基于规则
              • Domains defined by the rules that values must comply with in order to be valid.
      • 1.4.4. Data Modeling Schemes 数据建模的方法
        •  
        • 1. Relational 关系建模
          • First articulated by Dr. Edward Codd in 1970, relational theory provides a systematic way to organize data so that they reflected their meaning (Codd, 1970). This approach had the additional effect of reducing redundancy in data storage. Codd’s insight was that data could most effectively be managed in terms of two-dimensional relations. The term relation was derived from the mathematics (set theory) 集合理论 upon which his approach was based. (See Chapter 6.)
          • The design objectives for the relational model are to have an exact expression of business data and to have one fact in one place (the removal of redundancy).Relational modeling is ideal for the design of operational systems, which require entering information quickly and having it stored accurately (Hay, 2011).
          • Kinds
            • Information Engineering (IE) 信息工程
              • The most common form is IE syntax, with its familiar tridents or ‘crow’s feet’ to depict cardinality.
                • "crow's feet" 鸭掌线 to depict cardinality
            • Integration Definition for Information Modeling (IDEF1X) 集成定义
            • Barker Notation 巴克表示法
            • Chen Notation 陈表示法
        • 2. Dimensional 维度建模
          • The concept of dimensional modeling started from a joint research project conducted by General Mills and Dartmouth College in the 1960’s.33 In dimensional models, data is structured to optimize the query and analysis of large amounts of data. In contrast, operational systems that support transaction processing are optimized for fast processing of individual transactions
          • axis notation 轴表示法
            • 
          • Dimensional data models capture business questions focused on a particular
          • Both the relational and dimensional conceptual data models can be based on the same business process (as in this example with Admissions).
          • The difference is in the meaning of the relationships, where on the relational model the relationship lines capture business rules, and on the dimensional model, they capture the navigation paths needed to answer business questions.
          • Define
            • Fact Tables 事实表
              • Within a dimensional scheme, the rows of a fact table correspond to particular measurements and are numeric, such as amounts, quantities, or counts. Some measurements are the results of algorithms, in which case Metadata is critical to proper understanding and usage. Fact tables take up the most space in the database (90% is a reasonable rule of thumb), and tend to have large numbers of rows.
              • 22. A data model that consists of a single fact table linked to important concepts of the business is a A：Process Model 处理模型 B：Transition State Model 过渡态 C：Relational Model 关系模型 D：Machine Learning Model E：Dimensional Model 维度模型 正确答案：E 你的答案：C 解析：5.1.3. 在维度模型中，事实表（Fact Tables）的行对应于特定的数值型度量值。例如，金额、交易量或个数等。有些度量值是算法的结果，在这种情况下，元数据对于正确理解和使用至关重要。事实表占据了数据库的大部分空间（90%是一个合理的经验法则），并且往往具有大量的行。
              • 42. In Dimensional data models，which of these is Not true regarding measures 度量 A：Care must be taken if a measure is a snapshot figure B：Measures are found in Fact tables C：Measures should be numeric and additive 累加 D：Measures can always be added across all dimensions 总是所有维度 E：Just because a value is numerical does not mean it is a measure 正确答案：D 你的答案：E 解析：5.1.3题解：1）事实表。在维度横型中，事实表（Fact Tables）的行对应于特定的数值型度量值。例如，金额、交易量或个数等。有些度量值是算法的结果
            • Dimension Tables 维度表
              • Dimension tables represent the important objects of the business and contain mostly textual descriptions. Dimensions serve as the primary source for ‘query by’ or ‘report by’ constraints, by acting as the entry points or links into the fact tables. Dimensions are typically highly denormalized and typically account for about 10% of the total data.
              • contain mostly textual descriptions
              • ‘query by’ or ‘report by’
              • highly denormalized 高度反范式的
              • a unique identifier for each row
              • surrogate keys 代理键 and natural keys 自然键
              • Slowly changing dimensions (SCDs) 渐变类维度
              • The three main types of change are sometimes known by ORC 数据更新处理方式
                • Overwirte 新覆盖旧
                  • The new value overwrites the old value in place.
                • New Row 新行记录，旧行标记非当前值
                  • The new values are written in a new row, and the old row is marked as notcurrent.
                • New Column 指针取最新列值，后面列值舍弃
                  • Multiple instances of a value are listed in columns on the same row, and anew value means writing the values in the series one spot down to make space at the front for the newvalue. The last value is discarded.
              • 2. In a dimensional database model a dimension table contains A：units of measures for database attributes B：meta data that describes other data in the data model C：quantitative data about business activities D：descriptions of the subjects of the business E：all 正确答案：B 你的答案：D 解析：5.1.3：2）维度表。维度表（Dimension Tables）表示业务的重要对象，并且主要包含文字描述。维度是事实表的入口点或链接，充当“查询”或“报表”约束的主要来源。维度通常是高度反范式的，通常占总数据的10%左右。
            • Snowflaking 雪花模型
              • Snowflaking is the term given to normalizing the flat, single-table, dimensional structure in a star schema into the respective component hierarchical or network structures.
                • 16. A term is the term given to normalizing the fiat single-table，dimensional structure in a star schema into the respective component hierarchical or network structures. this term is called A：FCO-IM B：Dimension Tables C：Grain D：Snowflaking E：Conformed Dimensions 正确答案：D 你的答案：D 12章题解析：正确答案：D来源：5.1.3题解：3）雪花模型。雪花模型（Snowflaking）的含义是将星型模式中的平面、单表、维度结构规范为相应的组件层次结构或网络结构。
            • Grain 粒度
              • The term grain stands for the meaning or description of a single row of data in a fact table; this is the most detail any row will have.
            • Conformed Dimensions 一致性维度
              • Conformed dimensions are built with the entire organization in mind instead of just a particular project; this allows these dimensions to be shared across dimensional models, due to containing consistent terminology and values.
            • Conformed Facts 一致性事实
              • Conformed facts use standardized definitions of terms across individual marts. Different business users may use the same term in different ways.
        • 3. Object-Oriented, UML 面向对象的，统一建模语言
          • The Unified Modeling Language (UML) is a graphical language for modeling software. The UML has a variety of notations of which one (the class model) concerns databases. The UML class model specifies classes (entity types) and their relationship types (Blaha, 2013).
            • Class model 类模型 as notation 表示法
              • 
            • Characteristics
              • Attribute types (e.g., Date, Minutes) are expressed in the implementable application code language andnot in the physical database implementable terminology.
            • Class Operations
              • Public: Externally visible
              • Internally Visible: Visible to children Objects
              • Private: Hidden
            • Class diagram vs. ER diagram
        • 4. Fact-Based, FBM 基于事实的建模
          • Fact-Based Modeling, a family of conceptual modeling languages, originated in the late 1970s. These languages are based in the analysis of natural verbalization (plausible sentences) that might occur in the business domain. Fact-based languages view the world in terms of objects, the facts that relate or characterize those objects, and each role that each object plays in each fact. An extensive and powerful constraint system relies on fluent automatic verbalization and automatic checking against the concrete examples
          • Object Role Modeling (ORM or ORM2) 对象角色建模
            • Object‐Role Modeling (ORM) is a model‐driven engineering approach that starts with typical examples of required information or queries presented in any external formulation familiar to users, and then verbalizes these examples at the conceptual level, in terms of simple facts expressed in a controlled natural language.
          • Fully Communication Oriented Modeling (FCO-IM) 完全面向通信的建模
            • FCO-IM is similar in notation and approach to ORM. The numbers in Figure 43 are references to verbalizations of facts. For example, 2 might refer to several verbalizations including “Student 1234 has first name Bill.”
        • 5. Time-Based 基于时间建模
          • Data Vault 数据拱顶
            • 
            • The Data Vault is a detail-oriented, time-based, and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach, encompassing the best of breed between third normal form (3NF, to be discussed in Section 1.3.6) and star schema. Data Vaults are designed specifically to meet the needs of enterprise data warehouses. There are three types of entities: hubs, links, and satellites. The Data Vault design is focused around the functional areas of business with the hub representing the primary key. The links provide transaction integration between the hubs. The satellites provide the context of the hub primary key (Linstedt, 2012).
              • 37. A term is a detail-oriented, time-based and uniquely linked set of normalized tables that support one or more functional areas of business this term is called A：FCO-IM完全面向通信的建模（Fully Communication Oriented Modeling，FCO-IM） B：ORM2对象角色建模（Object Role Modeling，ORM或ORM2） C：ORM对象角色建模 D：Data Vault数据拱顶 E：FBM基于事实的建模（Fact-Based Modeling，FBM） 正确答案：D 你的答案：E 解析：5.1.3：数据拱顶（Data Vault）是一组支持一个或多个业务功能倾域，面向细节、基于时间且唯一链接的规范化表。
            • hubs 中心表
              • which represent the main concepts within a subject
            • links 链接表
              • which relates two hubs to each other
            • satellites 卫星表
              • provide the descriptive information on the hub concepts and can support varying types of history.
            • 30. The key concepts of the data vault 数据拱顶 are A：Hubs, links and satellites B：Keys, locks and combinations C：Facts, dimensions and time D：Hubs, links and planets E：Facts, links and satellites 正确答案：A 你的答案：A 解析：5.1.3：）数据拱顶。数据拱顶（Data Vault）是一组支持一个或多个业务功能领域，面向细节、基于时间且唯一链接的规范化表，数据拱顶模型是一种混合方式，综合了第三范式（3NF，将会在后面章节中讨论）和星型模式的优点。数据拱顶模型专门为满足企业数据仓库的需求而设计的，数据拱顶模型有3种类型的实体；中心表、锚接表和卫星表。
          • Anchor Modeling 锚建模
            • 
            • Anchor Modeling is a technique suited for information that changes over time in both structure and content. It provides graphical notation used for conceptual modeling similar to traditional data modeling, with extensions for working with temporal data. Anchor Modeling has four basic modeling concepts: anchors, attributes, ties, and knots. Anchors model entities and events, attributes model properties of anchors, ties model the relationships between anchors, and knots are used to model shared properties, such as states.
              • anchors 锚
              • attributes 属性
              • ties 连接
              • knots 节点
        • 6. NoSQL 非关系型数据库
          • NoSQL is a name for the category of databases built on non-relational technology.
            • Document databases 文档数据库
              • Instead of taking a business subject and breaking it up into multiple relational structures, document databases frequently store the business subject in one structure called a document. For example, instead of storing Student, Course, and Registration information in three distinct relational structures, properties from all three will exist in a single document called Registration.
            • Key-value databases 键值数据库
              • Key-value databases allow an application to store its data in only two columns (‘key’ and ‘value’), with the feature of storing both simple (e.g., dates, numbers, codes) and complex information (unformatted text, video, music, documents, photos) stored within the ‘value’ column.
            • Column-oriented databases 列数据库
              • Out of the four types of NoSQL databases, column-oriented is closest to the RDBMS. Both have a similar way of looking at data as rows and values. The difference, though, is that RDBMSs work with a predefined structure and simple data types, such as amounts and dates, whereas column-oriented databases, such as Cassandra, can work with more complex data types including unformatted text and imagery. In addition, column-oriented databases store each column in its own structure.
            • Graph databases 图形数据库
              • A graph database is designed for data whose relations are well represented as a set of nodes with an undetermined number of connections between these nodes. Examples where a graph database can work best are social relations (where nodes are people), public transport links (where nodes could be bus or train stations), or roadmaps (where nodes could be street intersections or highway exits). Often requirements lead to traversing the graph to find the shortest routes, nearest neighbors, etc., all of which can be complex and time-consuming to navigate with a traditional RDMBS. Graph databases include Neo4J, Allegro, and Virtuoso.
      • 1.4.5. Data Model Levels of Detail 数据模型的级别
        • Conceptual 概念模式 the real world view - Conceptual Data Model, CDM 概念数据模型 高度抽象便于理解
          • 
          • A conceptual data model captures the high-level data requirements as a collection of related concepts. It contains only the basic and critical business entities within a given realm and function, with a description of each entity and the relationships between entities.
            • 32. A term captures the high-level data requirements as a collection of related concepts. This term called A：Canonical data model B：conceptual data model C：logical data model D：physical data model E：subject Area model 正确答案：B 你的答案：B 解析：5.1.3：（1）概念数据模型概念数据横型（Conceptual Data Model，CDM）是用一系列相关主题域的集合来描述概要数据需求，概念数据模型仅包括给定的领域和职能中基础和关键的业务实体，同时也给出实体和实体之间关系的描述。
            • 41. The role of the Conceptual data model in the metadata repository is A：to summaries the key data subject areas for a business area at a high level of abstraction to enable the major data concepts to be understood 在较高的抽象层次上总结业务领域的关键数据主题领域，以便理解主要的数据概念 B：to agree the cardinality 基数 and optionality of relationships between all entities C：all of these D：None of the these E："to determine the primary alternate and foreign keys of entities” 正确答案：A 你的答案：A 解析：5.1.3：（1）概念数据横型概念数据横型（Conceptual Data Model，CDM）是用一系列相关主题域的集合来描述概要数据需求，概念数据模型仅包括给定的领域和职能中基础和关键的业务实体，同时也给出实体和实体之间关系的描述
          • For example, if we were to model the relationship between students and a school, as a relational conceptual data model using the IE notation
        • External 外模式 sub-sets of particular needs - Logical Data Model, LDM 逻辑数据模型 对数据需求的详细描述
          • 
          • A logical data model is a detailed representation of data requirements, usually in support of a specific usage context, such as application requirements. Logical data models are still independent of any technology or specific implementation constraints. A logical data model often begins as an extension of a conceptual data model.
            • 27. A term is a detailed representation of data requirements usually in support of a specific usage context, such as application requirements. This term is called A：Canonical data model 规范化 B：conceptual data model C：logical data model D：physical data model E：Subject Area model 主题域 正确答案：C 你的答案：C 解析：5.1.3：逻辑数据横型（Logical Data Model，LDM）是对数据需求的详细描述，通常用于支持特定用法的语境中（如应用需求）。逻辑数据模型不受任何技术或特定实施条件的约束。逻辑数据横型通常是从概念数据横型扩展而来。
          • In a relational logical data model, the conceptual data model is extended by adding attributes. Attributes are assigned to entities by applying the technique of normalization
          • A dimensional logical data model is in many cases a fully-attributed perspective of the dimensional conceptual data model, as illustrated in Figure 49. Whereas the logical relational data model captures the business rules of a business process, the logical dimensional captures the business questions to determine the health and performance of a business process.
            • 5. Design review of a logical data model should include an explanation of all of the following EXCEPT A：the major process access of entities B：each entity and its major attributes. C：the relationships between the entities D：the major process logic. E：none 正确答案：D 你的答案：B 解析：5.1.3：在关系逻辑数据模横型中，通过添加属性来扩展概念数据模型。属性通过应用规范化技术被分配给实体，如图5-20所示。每个属性和它所在实体的主键之间都有非常强的关系。例如，学校名称（School Name）与学校代码（School Code）有很强的关系，学校代码的每个值最多返回一个学校名称。
        • Internal 内模式 machine view - Physical Data Model, PDM 物理数据模型 如何存储数据的详细解决方案
          • 
          • A physical data model (PDM) represents a detailed technical solution, often using the logical data model as a starting point and then adapted to work within a set of hardware, software, and network tools. Physical data models are built for a particular technology. Relational DBMSs, for example, should be designed with the specific capabilities of a database management system in mind
            • 40. A term represents a detailed technical solution often using the logical data model as a starting point and then adapted to work within a set of hardware, software and network tools. This term is called A：Canonical data model B：conceptual data model C：logical data model D：physical data model E：Subject Area model 正确答案：D 你的答案：D 解析：5.1.3.：（3）物理数据横型物理数据横型（Physical Data Model，PDM）描述了一种详细的技术解决方案，通常以逻辑数据模型为基础，与某一类系统硬件、软件和网络工具相匹配。
        • Others
          • Canonical 规范模型
            • A variant of a physical scheme is a Canonical Model, used for data in motion between systems. This model describes the structure of data being passed between systems as packets or messages. When sending data through web services, an Enterprise Service Bus (ESB), or through Enterprise Application Integration (EAI), the canonical model describes what data structure the sending service and any receiving services should use. These structures should be designed to be as generic as possible to enable re-use and simplify interface requirements.
              • 33. A term is a common model used by an organization or data exchange group that standardizes the format in which data will be shared this term is called A：ESB B：Canonical data model 规范 C：SOA D：EDI 电子数据交换 E：Software engineering 正确答案：B 你的答案：A 解析：5.1.3.：1）规范模型。规范横型（Canonical Model）是物理横型的一个变种，用于描述系统之间的数据移动。该横型描述了在系统之间作为数据报或消息传递的数据结构。当通过Web服务、企业服务总线（ESB）或企业应用程序集成（EAI）发送数据时，规范模型描述了发送服务和接收服务应该使用的数据结构。这些结构的设计应尽可能通用，以实现重用和简化接口需求。
          • Views 视图
            • A view is a virtual table. Views provide a means to look at data from one or many tables that contain or reference the actual attributes. A standard view runs SQL to retrieve data at the point when an attribute in the view is requested. An instantiated (often called ‘materialized’) view runs at a predetermined time. Views are used to simplify queries, control data access, and rename columns, without the redundancy and loss of referential integrity due to denormalization.
              • 38. A term is a virtual table in database this term is called A：index B：Trigger C：table D：view E：document 正确答案：D 你的答案：D 解析：5.1.3.：2）视圈。视图（Views）是虚拟表，它提供了一种从多张包含或引用实际属性的表中查看数据的方法。当请求视图中的一个属性时，标准视图会运行SQL来检索数据。
          • Partitioning 分区
            • Partitioning refers to the process of splitting a table. It is performed to facilitate archiving and to improve retrieval performance. Partitioning can be either vertical (separating groups of columns) or horizontal (separating groups of rows).
            • Vertically split 垂直分割
            • Horizontally split 水平分割
          • Denormalization 逆规范化
            • Denormalization is the deliberate 权衡后 transformation of normalized logical data model entities into physical tables with redundant or duplicate data structures. In other words, denormalization intentionally puts one attribute in multiple places.
              • 39. A term is the deliberate 权衡后 transformation of normalized logical data model entities into physical tables with redundant or duplicate data structures this term is called A：Canonical B：embedding C：normalized D：Denormalization E：Normalization 正确答案：D 你的答案：D 解析：5.1.3：4）逆规范化。逆规范化（Denormalization）是将符合范式规则的逻辑数据模型经过镇重考虑后，转换成一些带冗余数据的物理表。换言之，逆规范化有意将一个属性放在多个位置，将数据逆规范化有很多原因，最重要的是提高性能
            • There are several reasons to denormalize data. The first is to improve performance by:
              • 1. Combining data from multiple other tables in advance to avoid costly run-time joins
              • 2. Creating smaller, pre-filtered copies of data to reduce costly run-time calculations and/or table scans of large tables
              • 3. Pre-calculating and storing costly data calculations to avoid run-time system resource competition
            • Although the term denormalization is used in this section, the process does not apply just to relational data models.
              • For example, one can denormalize in a document database, but it would be called something different – such as embedding.嵌入
              • In dimensional data modeling, denormalization is called collapsing 折叠 or combining. 合并
              • If each dimension is collapsed into a single structure, the resulting data model is called a Star Schema 星型模型 (see Figure 51).
              • If the dimensions are not collapsed, the resulting data model is called a Snowflake 雪花模型 (See Figure 49).
      • 1.4.6. Normalization 规范化
        • Normalization is the process of applying rules in order to organize business complexity into stable data structures. The basic goal of normalization is to keep each attribute in only one place to eliminate redundancy and the inconsistencies that can result from redundancy. The process requires a deep understanding of each attribute and each attribute’s relationship to its primary key.
          • 36. A term is the process of applying rules in order to organize business complexity into stable data structures. This term is called A：Canonical B：embedding C：normal D：Normalization 规范化 E：Denormalization 正确答案：D 你的答案：D 解析：5.1.3.：6.规范化规范化（Normalization）是运用规则将复杂的业务转化为规范的般螺结构的过程。范式化的基本目标是保证每个属性只在一个位置出现，以消除沉余或沉余导数的不一致性。整个过程需骤深入理解每个属性，以及每个属性与主键的关系。规范化规则根据主键和外键整理属性。规范化规则可归类到不同规范层次，对每一个层次可应用更细的方式和规范性来搅素正确的主键和外键。
        • First normal form (1NF) 第一范式
          • Ensures each entity has a valid primary key, and every attribute depends on the primary key; removes repeating groups, and ensures each attribute is atomic 原子化 (not multi-valued).
          • 1NF includes the resolution of many-to-many relationships with an additional entity often called anassociative entity.
        • Second normal form (2NF)
          • Ensures each entity has the minimal primary key and that every attributedepends on the complete primary key.
        • Third normal form (3NF)
          • Ensures each entity has no hidden primary keys and that each attributedepends on no attributes outside the key (“the key, the whole key and nothing but the key”).
        • Boyce / Codd normal form (BCNF)
          • Resolves overlapping composite candidate keys.
          • A candidatekey is either a primary or an alternate key.
          • ‘Composite’ means more than one (i.e., two or moreattributes in an entity’s primary or alternate keys), and ‘overlapping’ means there are hidden businessrules between the keys.
        • Fourth normal form (4NF)
          • Resolves all many-to-many-to-many relationships (and beyond) in pairs until they cannot be broken down into any smaller pieces.
        • Fifth normal form (5NF)
          • Resolves inter-entity dependencies into basic pairs, and all joindependencies use parts of primary keys.
        • The term normalized model usually means the data is in 3NF
      • 1.4.7. Abstraction 抽象化
        • Abstraction is the removal of details in such a way as to broaden applicability to a wide class of situations while preserving the important properties and essential nature from concepts or subjects.
          • 35. A term is the removal of details in such a way as to broaden applicability to a wide class of situations while preserving the important properties and essential nature from concepts or subjects. This term is called A：Canonical B：embedding C：Normalization D：Abstraction 抽象 E：Denormalization 正确答案：D 你的答案：D 解析：5.1.3：抽象化抽象化（Abstraction）就是将细节移除，这样可以在更广泛的情况下扩展适用性，同时保留概念或主题的重要和本质属性
        • includes
          • generalization 泛化
            • Generalization groups the common attributes and relationships of entities into supertype 超类 entities
          • specialization 特化
            • specialization separates distinguishing attributes within an entity into subtype 子类 entities
        • 25. The necessity of representing organizational data at different levels of abstraction is: A：because most chief data officers don't have the technical background to be held accountable for complex data diagrams. B：because most architects want to deploy a complete suite of drawings for project deliverables C：because most database administrators need specifications to build databases with appropriate response times D：because most organizations need to accommodate to the different points of view of in E：because most organizations have more data than individual people can comprehend understand and make decisions about." 正确答案：E 你的答案：D 解析：因为数据量大，所以要分级，要抽象
  • 2. Activities
    • 2.1. Plan for Data Modeling 规划数据建模
      • 2.1.1. A plan for data modeling contains tasks such as
        • 1. evaluating organizational requirements 评估组织需求
        • 2. creating standards 制定建模标准
        • 3. determining data model storage 明确数据模型存储管理
      • 2.1.2. Deliverables
        • 1. Diagram 图表
          • A data model contains one or more diagrams. The diagram is the visual that captures therequirements in a precise form
          • level of details 层级
          • scheme 模型
          • notation 表示法
        • 2. Definitions 定义
          • Definitions for entities, attributes, and relationships are essential to maintaining theprecision on a data model
        • 3. Issues and outstanding questions 争议和未决问题
          • Frequently the data modeling process raises issues and questionsthat may not be addressed during the data modeling phase.
        • 4. Lineage 血缘关系
          • For physical and sometimes logical data models, it is important to know the data lineage, that is, where the data comes from.
            • First, the data modeler will obtain a very strong understanding of the data requirements and therefore is in the best position to determine the source attributes.
            • Second, determining the source attributes can bean effective tool to validate the accuracy of the model and the mapping
            • 21. A lineage data tool provides A：the capture and maintenance of source structures for each attribute on the data mode B：ancestorial 祖先 origin of data DNA C：a temporal distortion 时间失真 of data values across systems D：a clean line between columns in the same entity E：scope for reporting requirements 正确答案：B 你的答案：B 解析：5.2.1：4）血缘关系（Lineage）。对于物理模型（有时是逻辑数据模型）来说，了解数据血缘关系是非常重要的。血缘关系是指数据从哪里来，经过什么样的加工，变成了什么样的结果的脉络关系。一般而言，血缘关系会以来源/目标映射的形式呈现，这样就可以了解到源系统的属性以及它们如何被迁移至目标系统。
        • 5. 23. The deliverables for a data modelling process does not have to include A：one or more diagrams B：the steps in the business process that use the data 使用数据的步骤 C：issues and outstanding questions D：lineage describing where the data came from E：definitions for entities attributes and relationships 正确答案：B 你的答案：B 解析：5.2.1. B使用数据的步骤，是流程图用到的
    • 2.2. Build the Data Model
      • 2.2.1. Modeling is a very iterative 反复迭代的 process
        • 
        • 11. which of the following data modeling activities is usually the most time consuming? A：Determining requirements of stakeholders and business subject matter experts B：Translating the E-R diagram (s) into the physical database C：specifying the scope of the model D：Defining the entities，attributes and relationships E：Domain 正确答案：D 你的答案：D 解析：D为核心设计阶段，需要根据需求做各种权衡
      • 2.2.2. Forward Engineering 正向工程
        • Forward engineering is the process of building a new application beginning with the requirements.
          • The CDM is completed first to understand the scope of the initiative and the key terminology within that scope.
          • Then the LDM is completed to document the business solution
          • followed by the PDM to document the technical solution.
        • CDM
          • 1. Select Scheme
          • 2. Select Notation
          • 3. Complete Initial CDM
          • 4. Collect the highest-level concepts (nouns) that exist for the organization
          • 5. Collect the activities (verbs) that connect these concepts
          • 6. Incorporate Enterprise Terminology
          • 7. Obtain Sign-off
        • LDM
          • 1. Analyze Information Requirements
          • 2. Analyze Existing Documentation
          • 3. Add Associative Entities 添加关联实体
            • Associative entities are used to describe Many-to-Many (or Many-to-Many-to-Many, etc.) relationships
            • An associative entity takes the identifying attributes from the entities involved in the relationship, and puts them into a new entity that just describes the relationship between the entities. This allows the addition of attributes to describe that relationship, like in effective and expiration dates.
            • In dimensional modeling, associative entities usually become fact tables.
          • 4. Add Attributes
          • 5. Assign Domains
          • 6. Assign Keys
        • PDM
          • 1. Logical data models require modifications and adaptations in order to have the resulting design perform well within storage applications.
          • 2. Resolve Logical Abstractions 解决逻辑抽象
            • Subtype absorption 子类型吸收
              • The subtype entity attributes are included as nullable columns into a tablerepresenting the supertype entity.
            • Supertype partition 超类型分区
              • The supertype entity’s attributes are included in separate tables created for eachsubtype.
          • 3. Add Attribute Details
            • such as the technical name of each table and column (relational databases), or file and field (non-relational databases), or schema and element (XML databases).
            • Define the physical domain, physical data type, and length of each column or field.
            • Add appropriate constraints (e.g., nullability and default values) for columns or fields, especially for NOT NULL constraints.
          • 4. Add Reference Data Objects
            • Create a matching separate code table 创建匹配的单独代码表
              • Depending on the model, these can be unmanageablynumerous.
            • Create a master shared code table 创建主共享代码表
              • For models with a large number of code tables, this can collapsethem into one table; however, this means that a change to one reference list will change the entiretable. Take care to avoid code value collisions as well.
            • Embed rules or valid codes into the appropriate object's definition 嵌入到对象
              • Create a constraint in theobject definition code that embeds the rule or list. For code lists that are only used as reference for oneother object, this can be a good solution.
          • 5. Assign Surrogate Keys
            • If a surrogate key is assigned to be the primary key of a table, make sure there is an alternate key on the original primary key.
          • 6. Denormalize for Performance 逆规范化
            • In some circumstances, denormalizing or adding redundancy can improve performance so much that it outweighs the cost of the duplicate storage and synchronization processing.
            • Dimensional structures are the main means of denormalization.
          • 7. Index for Performance
            • An index is an alternate path for accessing data in the database to optimize query (data retrieval) performance.
            • Indexes can be unique or non-unique, clustered or non-clustered, partitioned or non-partitioned, single column or multi-column, b-tree or bitmap or hashed.
          • 8. Partition for Performance
            • Ideally, partitioning on a date key is recommended
            • Great consideration must be given to the partitioning strategy of the overall data model (dimensional) especially when facts contain many optional dimensional keys (
          • 9. Create Views
            • Views can be used to control access to certain data elements, or to embed common join conditions or filters to standardize common objects or queries.
            • Views themselves should be requirements-driven.
      • 2.2.3. Reverse Engineering 逆向工程
        • Reverse engineering is the process of documenting an existing database.
          • The PDM is completed first to understand the technical design of an existing system
          • followed by an LDM to document the business solution that the existing system meets
          • followed by the CDM to document the scope and key terminology within the existing system.
    • 2.3. Review the Data Models
      • 2.3.1. Techniques such as time-to-value 价值实现时间, support costs 支持成本, and data model quality validators 数据模型质量验证 such as the Data Model Scorecard 数据模型计分卡
      • 2.3.2. 18. When reviewing a data model with management，which of the following is Not a good approach? A：Generalize the model by removing trivial entities and relationships B：Show entities, attributes，and relationships in a diagram C：use a subject area drawing D：Highlight any complex drawing by use of color or shading to emphasize the important points E：none 正确答案：B 你的答案：A 解析：5.2.3.：B只是单纯展示概念数据模型，应结合综合其他模型一起审核。 和IT的其他领域一样，需要通过持续改进实践来控制模型质量。诸如价值实现时间、支持成本和数据模型质量验证器（如数据模型记分卡）
    • 2.4. Maintain the Data Models
      • 2.4.1. they need to be kept current
      • 2.4.2. Updates to the data model need to be made when requirements change and frequently when business processes change.
      • 2.4.3. often when one model level needs to change, a corresponding higher level of model needs to change.
  • 3. Tools
    • 3.1. Data Modeling Tools
      • 3.1.1. Data modeling tools are software that automate many of the tasks the data modeler performs.
      • 3.1.2. These entry-level tools also support rubber banding 橡皮筋, which is the automatic redrawing of relationship lines when entities are moved.
        • PowerDesigner
        • ERwin
        • ER/Studio
    • 3.2. Lineage Tools
      • 3.2.1. A lineage tool is software that allows the capture and maintenance of the source structures for each attribute on the data model.
        • 28. A data lineage tool enables a user to: A：track the data from source system to a target database understanding its transformations B：visualize how the data gets to the data lake C：track the historical changes to a data value D：line up the data to support sophisticated glossary management E：enables rapid development of dashboard reporting 正确答案：A 你的答案：A 解析：：5.3.2数据血缘工具数据血缘工具是允许捕获和维护数据模型上每个属性的源结构变化的工具。通过这些工具可实现变更影响分析，也就是说，可以使用它们来查看一个系统的变化或系统的一部分中的变化是否对另一个系统产生影响。例如，属性总销售额可能来自多个应用程序，需要计算才能填充——血缘工具将存储此信息。
        • 29. A complexity in documenting data lineage is A：choosing which content management software to use B：different data element names and formats C：establishing data quality metrics D：conflicting 冲突 application requirements from data owners E：identifying source databases 正确答案：B 你的答案：B 解析： 5.3.2数据血缘工具数据血缘工具是允许捕获和维护数据模型上每个属性的源结构变化的工具。通过这些工具可实现变更影响分析，也就是说，可以使用它们来查看一个系统的变化或系统的一部分中的变化是否对另一个系统产生影响。例如，属性总销售额可能来自多个应用程序，需要计算才能填充——血缘工具将存储此信息。
      • 3.2.2. These tools enable impact analysis; that is, one can use them to see if a change in one system or part of system has effects in another system.
    • 3.3. Data Profiling Tools 数据分析工具
    • 3.4. Metadata Repositories 元数据资料库
      • 3.4.1. A Metadata repository is a software tool that stores descriptive information about the data model, including the diagram and accompanying text such as definitions, along with Metadata imported from other tools and processes
      • 3.4.2. The repository itself should enable Metadata integration and exchange.
      • 3.4.3. Even more important than storing the Metadata is sharing the Metadata.
      • 3.4.4. Metadata repositories must have an easily accessible way for people to view and navigate the contents of the repository.
      • 3.4.5. Data modeling tools generally include a limited repository.
    • 3.5. Data Model Patterns 数据模型模式
      • 3.5.1. Data model patterns are reusable modeling structures that can be applied to a wide class of situations.
        • Elementary patterns 基本模式 are the ‘nuts and bolts’ of data modeling. They include ways to resolve many-to-many relationships, and to construct self-referencing hierarchies.
        • Assembly patterns 套件模式 represent the building blocks that span the business and data modeler worlds.
        • Integration patterns 整合模式 provide the framework for linking the assembly patterns in common ways
    • 3.6. Industry Data Models
      • 3.6.1. Industry data models are data models pre-built for an entire industry, such as healthcare, telecom, insurance, banking, or manufacturing.
        • ARTS (for retail)
        • SID (for communications)
        • ACORD (for insurance)
      • 3.6.2. In some cases, it can be a reference for an organization's in-progress efforts to help the modelers make models that are more complete.
      • 3.6.3. Any purchased data model will need to be customized to fit an organization, as it will have been developed from multiple other organizations’ needs.
      • 3.6.4. it can merely save the data modeler some data entry effort for annotated common elements.
        • 19. An industry data model is used to: A：quickly establish a common vocabulary to aid the integration of disparate systems B：replace the need for experienced data modelers C：confirm the correctness of a data model D：reduce the data integration requirement across an industry E：derive industry process models 正确答案：D 你的答案：A 解析：题解：5.3.6行业数据模型行业数据模型是为整个行业预建的数据模型，它们可以作为工作参考，帮建模人员制作更完整的模型。有时，它只能帮助数据建模人员节约一些公共元素的录入工作。
  • 4. Best Practices
    • 4.1. Best Practices in Naming Conventions 命名约定的最佳实践
      • 4.1.1. The ISO 11179 Metadata Registry, an international standard for representing Metadata in an organization, contains several sections related to data standards, including naming attributes and writing definitions.
        • naming attributes 命名属性
        • writing definitions 书写定义
        • 10. The recommended standard for a data element naming convention includes A：prime word 主词, modifier(s) 修饰词.class word 类词 B：consistency check(s)attribute(s).identifier. C：meta-data, category data design D：keyword(s).cross reference(s).aliases E：ALL 正确答案：A 你的答案：A 解析：5.4.1：A最合理。逻辑名称对业务用户应具有意义，应尽可能使用完整的单词，并避免使用除最熟悉的缩写之外的单词。物理名称必须符合DBMS允许的最大长度，因此必要时将使用缩写。逻辑名称通常情况下不允许使用任何的分隔符对单词进行分隔，但物理名称通常使用下划线作为单词分隔符。
      • 4.1.2. Publish data model and database naming standards for each type of modeling object and database object. Naming standards are particularly important for entities, tables, attributes, keys, views, and indexes.
        • Names should be unique and as descriptive as possible.
        • Naming standards should minimize name changes across environments.
        • They can also show which attributes and columns are quantitative rather than qualitative, which can be important when analyzing the contents of those columns.
      • 4.1.3. Logical names should be meaningful to business users, using full words as much as possible and avoiding all but the most familiar abbreviations.
        • While logical names use blank spaces as separators between words,
        • 15. Good habits in defining data include all of the following EXCEPT A：creating inheritance of definition B：using business based terms and meanings C：establishing a data naming taxonomy D：adopting short definitions E：all 正确答案：D 你的答案：D 解析：正确答案：D来源：5.4.1题解： 逻辑名称对业务用户应具有意义，应尽可能使用完整的单词，并避免使用除最熟悉的缩写之外的单词。物理名称必须符合DBMS允许的最大长度，因此必要时将使用缩写。逻辑名称通常情况下不允许使用任何的分隔符对单词进行分隔，但物理名称通常使用下划线作为单词分隔符。
      • 4.1.4. Physical names must conform to the maximum length allowed by the DBMS, so use abbreviations where necessary.
        • physical names typically use underscores as word separators.
    • 4.2. Best Practices in Database Design 数据库设计中的最佳实践
      • 4.2.1. PRISM
        • 1. Performance and ease of use 性能易用性
        • 2. Reusability 可复用性
        • 3. Integrity 完整性
          • 14. Business considerations for data privacy include all of the following EXCEPT data A：storage. B：destruction 销毁 C：usage D：integrity 完整 E：All 正确答案：D 你的答案：D 解析：5.4.2：D和数据横型设计相关。3）完整性（Integrity）。无论语境如何，数据应始终具有有效的业务含义和价值，并且应始终反映业务的有效状态。实施尽可能接近数据的数据完整性约束，并立即检测并报告数据完整性约束的违规行为。
        • 4. Security 安全性
        • 5. Maintainability 可维护性
  • 5. Data Model Governance
    • 5.1. Data Model and Design Quality Management
      • 5.1.1. Develop Data Modeling and Design Standards
        • 1. A list and description of standard data modeling and database design deliverables
        • 2. A list of standard names, acceptable abbreviations, and abbreviation rules for uncommon words, thatapply to all data model objects
        • 3. A list of standard naming formats for all data model objects, including attribute and column classwords
        • 4. A list and description of standard methods for creating and maintaining these deliverables
        • 5. A list and description of data modeling and database design roles and responsibilities
        • 6. A list and description of all Metadata properties captured in data modeling and database design,including both business Metadata and technical Metadata. For example, guidelines may set theexpectation that the data model captures lineage for each attribute.
        • 7. Metadata quality expectations and requirements (see Chapter 13)
        • 8. Guidelines for how to use data modeling tools
        • 9. Guidelines for preparing for and leading design reviews
        • 10. Guidelines for versioning of data models
        • 11. Practices that are discouraged
      • 5.1.2. Review Data Model and Database Design Quality
      • 5.1.3. Manage Data Model Versioning and Integration
        • 1. Why the project or situation required the change
        • 2. What and How the object(s) changed, including which tables had columns added, modified, orremoved, etc.
        • 3. When the change was approved and when the change was made to the model (not necessarily whenthe change was implemented in a system)
        • 4. Who made the change
        • 5. Where the change was made (in which models)
        • 6. 8. which of the following statements is MOST true? A：An enterprise data model should NOT be modified as a result of subsequent projects B：a data model is produced at the beginning of a project and is NOT subsequently changed C：since an enterprise data model is static, change control procedures are NOT necessary. D：Change control is used for any modeling effort E：AIl 正确答案：D 你的答案：D 解析：5.5.1：3.管理数据模型版本与集成对数据模型和其他设计规范需要谨慎的变更控制，就像需求规范和其他SDLC可交付成果一样。注意对数据横型的每次更改，需要以时间线记录变更内容。如果更改影响到了逻辑数据模型，如新的或更改了的业务故据要求，则需要数据分析师或架构师审核并批准对模型的更改。每个变更都应该予以记录。
    • 5.2. Data Modeling Metrics 度量指标
      • 5.2.1. How well does the model capture the requirements?
        • 9. After a reasonable effort has been expended to produce a data model for a project which of the following would be the most useful technique to validate that the data model supports the project adequately? A：Check to see what entities have been created for other projects B：Review the legal implications of the model C：Model the effect that events in the business have on the model D：Prototype the application E：All 正确答案：C 你的答案：C 解析：5.5.2.数据模型计分卡
      • 5.2.2. How complete is the model?
        • completeness of requirements
        • completeness of Metadata.
      • 5.2.3. How well does the model match its scheme?
      • 5.2.4. How structurally sound is the model? 结构如何
      • 5.2.5. How well does the model leverage generic structures? 通用性如何
      • 5.2.6. How well does the model follow naming standards?
      • 5.2.7. How well has the model been arranged for readability?
      • 5.2.8. How good are the definitions?
      • 5.2.9. How consistent is the model with the enterprise?
      • 5.2.10. How well does the metadata match the data?
  • 6. Works Cited / Recommended
    • 6.1. 3. which of the following is NOT a design artifact 人工制品? A：A descriptive representation of any complex object B：A data model C：An architectural representation D：Database logs E：all 正确答案：D 你的答案：D 解析：ABC是人设计的，D是系统产出的
    • 6.2. 6. A top-down approach to database design is A：entity relationship analysis B：view integration C：attribute synthesis D：data normalization E：All 正确答案：A 你的答案：E 解析：4.1.3：自上而下是从主题域开始，先设计主题，再逐步设计下层横型。而采用自下而上的方法时，主题域结构则是基于现有逻辑数据横型向上提炼抽象而成。
    • 6.3. 7. which of the following BEST describes the role of a data intermediary 中间人? A：Someone who decides what data is important to the business B：Someone who is responsible for managing the meta-data repository C：someone who receives data in one form and transcribes it into another for D：Someone who is responsible for translating business requirements into a database design E：All 正确答案：C 你的答案：C 解析：5.5：数据分析人员和设计人员作为信息消费者（具有数据业务需求的人）和数据生产者之间的中介，他们必须平衡信息消费者的数据使用要求和数据生产者的应用要求。
    • 6.4. 13. which of the following is NOT true about data modeling? A：lt is a concise way 简洁的方式of representing all of the business rules in the enterprise B：lt is a useful graphical depiction C：lt is used to recognize and represent patterns 模式 of relationships between entities. D：It helps people to visualize and understand a business. E：All 正确答案：A 你的答案：A 解析：A错误，业务规则和数据模型不处于一个层次，业务规则可以映射为对数据横型的CRUD操作
    • 6.5. 16. For business intelligence business data stewards help with all of the following EXCEPT A：Bl issue B：dimensional model development 开发模型 C：Bl requirements D：management metrics E：Data quality Metrics 正确答案：B 你的答案：E 解析：5.1.1. B选项中的参与者是业务分析师和数据建模师，不是数据管家，以及BI业务应用；B是具体执行的任务不是数据管家的工作。
    • 6.6. 17. The data in legacy systems 停产的系统 that satisfy business requirements and do not have data models may be leveraged by which of the following options? A：Instantiate 实例化 the data into objects B：Map 映射 the data elements to the appropriate data attributes in the Enterprise Data Model C：confine 限制 data management to the legacy system to minimize impact on the enterprise data mode D：Develop a data service. E：DaaS 正确答案：B 你的答案：C 解析：C映射并应用数据模型，D过于消极与保守
    • 6.7. 26. Precise data definitions are important to avoid all of the following EXCEPT A：informality and inconsistency B：uncertainty and lack of quality C：data degradation 数据降级 D：meaningful entities and attributes. 有意义的实体和属性 E：all 正确答案：D 你的答案：C 解析：D的逻辑反了
    • 6.8. 31. The acroymn CAP stands for A：Consistency 一致性, Availability 可用性, Partition Tolerance 分区容错 B：Consistency, Access, Partition Tolerance C：Consistency, Accuracy, Partition Tolerance D：Completeness, Availability, Partition Tolerance E：Completeness, Access, Partition Tolerance 正确答案：A 你的答案：B 解析：6.1.3.：（3）CAP定理（也称为“布鲁尔定理”）是集中式系统在朝着分布式的系统方向发展过程中提出的理论，CAP定理指的是分布式系统不可能同时满足ACID的所有要求，系统规横越大，满足的要求点越少。分布式系统必须在各种属性（要求）间进行权衡。1）一致性（Consistency）.系统必须总是按照设计和预期的方式运行，2）可用性（Availability）.请求发生时系统时刻都保持可用状态，并对请出响应，3）分区容错（Partition Tolerance），
    • 6.9. 34. When trying to integrate a large number of systems，the integration complexities can be reduced by A：the use of SQL. B：the use of a common data model C：tackling the largest systems first. D：using data quality measures and targets E：clear business specification and priorities. 正确答案：B 你的答案：B 解析：数据建模是发现、分析和确定数据需求的过程，用一种称为数据横型的精确形式表示和传递这些数据需求。数据建模是数据管理的一个重要组成部分，建模过程中要求组织发现并记录数据组合的方式。
• Chapter 6: Data Storage and Operations 数据存储与操作
  • 
  • 1. Introduction
    • 1.1. Data Storage and Operations includes the design, implementation, and support of stored data, to maximize its value throughout its lifecycle, from creation/acquisition to disposal
      • 1.1.1. Database support 数据库操作支持
        • focuses on activities related to the data lifecycle, from initial implementation of adatabase environment, through obtaining, backing up, and purging data. It also includes ensuring thedatabase performs well. Monitoring and tuning are critical to database support.
      • 1.1.2. Database technology support 数据库技术支持
        • includes defining technical requirements that will meet organizational needs, defining technical architecture, installing and administering technology, and resolving issuesrelated to technology.
      • 1.1.3. Database administrators (DBAs) 数据库管理员
    • 1.2. Business Drivers
      • 1.2.1. Companies rely on their information systems to run their operations. Data Storage and Operations activities are crucial to organizations that rely on data. Business continuity is the primary driver of these activities.
        • 18. A primary business driver 主要业务驱动 of data storage and operations is A：business rules. B：hardware sales C：business process optimization D：business continuity E：business data modelling 正确答案：D 你的答案：D 解析：6.1. 数据存储与操作活动对于依赖数据的企业来说非常关键，这些活动的主要驱动因素是业务连续性。
    • 1.3. Goals and Principles
      • 1.3.1. Goals:
        • 1. Manage availability of data throughout the data lifecycle.
        • 2. Ensure the integrity of data assets.
        • 3. Manage performance of data transactions.
        • 20. The goals of data storage and operations are: A：managing user experience，ensuring consistency and managing glossaries B：managing availability ensuring consistency and managing glossaries C：managing availability ensuring integrity and managing performance D：managing accessibility ensuring authorized access and managing performance E：managing user experience ensuring data access and managing data lifecycles 正确答案：C 你的答案：C 解析：6.1.2题解：数据存储与操作的目标包括：1）在整个数据生命周期中管理数据的可用性。2）确保数据资产的完整性。3）管理数据交易事务的性能。
      • 1.3.2. Principles
        • 1. Identify and act on automation opportunities
          • Automate database development processes,developing tools, and processes that shorten each development cycle, reduce errors and rework, andminimize the impact on the development team.
        • 2. Build with reuse in mind
          • Develop and promote the use of abstracted and reusable data objects thatprevent applications from being tightly coupled to database schemas (the so-called ‘object-relational impedance mismatch’ 对象关系阻抗不匹配).
        • 3. Understand and appropriately apply best practices
          • DBAs should promote database standards andbest practices as requirements, but be flexible enough to deviate from them if given acceptable reasonsfor these deviations.
        • 4. Connect database standards to support requirements
          • For example, the Service Level Agreement(SLA) can reflect DBA-recommended and developer-accepted methods of ensuring data integrity anddata security.
        • 5. Set expectations for the DBA role in project work
          • Ensuring project methodology includesonboarding the DBA in project definition phase can help throughout the SDLC.
    • 1.4. Essential Concepts
      • 1.4.1. Database Terms 数据库术语
        • Database 数据库
          • Any collection of stored data
        • Instance 实例
          • An execution of database software controlling access to a certain area of storage
        • Schema 模式
          • A subset 子集 of a database objects contained within the database or an instance
        • Node 节点
          • An individual computer hosting either processing or data as part of a distributed database.
        • Database abstraction 数据抽象
          • means that a common application interface (API) is used to call database function
      • 1.4.2. Data Lifecycle Management 数据生命周期管理
        • DBAs maintain and assure the accuracy and consistency of data over its entire lifecycle through the design, implementation, and usage of any system that stores, processes, or retrieves data.
        • includes implementing policies and procedures for acquisition, migration, retention, expiration, and disposition of data.
          • The DBA should have a back out plan to reverse changes in case of problems.
      • 1.4.3. Administrators 管理员
        • DBAs do not exclusively perform all the activities of Data Storage and Operations. Data stewards, data architects, network administrators, data analysts, and security analysts participate in planning for performance, retention, and recovery. These teams may also participate in obtaining and processing data from external sources.
        • Production DBA
          • responsibility
            • 1. Ensuring the performance and reliability of the database, through performance tuning, monitoring, error reporting, and other activities
              • 1. A major focus of the database administration Operations function is A：database integrity. B：conceptual data architecture C：data management policies D：construction of database inquiries 调查 E：database security 正确答案：A 你的答案：A 解析：1.3.3. ：3）活动。②控制活动（C）。持续地确保数据质量，以及数据存取和使用的完整性、可靠性和安全性。
            • 2. Implementing backup and recovery mechanisms to ensure data can be recovered if lost in any circumstance
              • 7. The primary operational responsibility for DBAs that has the most data Governance overlap is A：creating mechanisms for clustering of data 数据聚类 B：ensuring performance and reliability of databases C：implementing appropriate backup and recovery mechanism 实时恰当的备份和恢复 D：scheduling run stats and reorganizations. E：all 正确答案：C 你的答案：B 解析：6.1.3：（1）生产DBA生产DBA主要负责数据操作管理，包括：1）通过性能调优、监控、错误报告等活动，确保数据库的性能及可靠性。2）通过建立备份与恢复机制，确保在任何意外情况下数据能够被恢复。3）通过建立集群和容错机制，确保数据连续可用
            • 3. Implementing mechanisms for clustering 集群 and failover 容器 of the database, if continual data availability datais a requirement
            • 4. Executing other database maintenance activities, such as implementing mechanisms for archiving data
          • deliverables
            • 1. A production database environment, including an instance of the DBMS (Database ManagementSystem) on the supporting server, of a sufficient size and capacity to ensure adequate performance,configured for the appropriate level of security, reliability, and availability. Database System Administration is responsible for the DBMS environment.
            • 2. Mechanisms and processes for controlled implementation of changes to databases in the productionenvironment
            • 3. Mechanisms for ensuring the availability, integrity, and recoverability of data in response to all circumstances that could result in loss or corruption of data
            • 4. Mechanisms for detecting and reporting any error that occurs in the database, the DBMS, or the data server
            • 5. Database availability, recovery, and performance in accordance with service level agreements
            • 6. Mechanisms and processes for monitoring database performance as workloads and data volumes vary
          • 5. which of the following services is typically NOT provided by Database Administration Operations? A：Implementing data standards B：Validating transaction data C：Planning for data distribution D：Validating data models E：AIl 正确答案：B 你的答案：C 解析：：B是数据库内核开发工程师做验证 6.1.2.数据库管理员（DBA）是数据专业中最常见、也是最广泛被接纳的角色。DBA在数据存储与操作活动中承担着主导角色，在数据安全活动（参见第7章）及物理模型建模、数据库设计活动（参见第5章）中也是关键的角色。DBA为开发环境、测试环境、QA环境及其他特殊数据库环境提供支持。
        • Application DBA
          • An application DBA is responsible for one or more databases in all environments , as opposed to database systems administration for any of these environments.
        • Procedural and Development DBAs
          • Procedural DBAs lead the review and administration of procedural database objects.
          • Development DBAs focus on data design activities including creating and managing special use databases
          • In many cases, these two functions are combined under one position.
        • Network Storage Administrators, NSA 网络存储管理员
          • Network Storage Administrators are concerned with the hardware and software supporting data storage arrays.
        • 10. A data owner has responsibility and authority for A：distributions and retention of data B：regulations policies，knowledge of data privacy laws，business rules and business objectives C：quality, access, distribution, and business definitions. D：business definitions and data security management E：all 正确答案：C 你的答案：A 解析：ABD其中一些是DBA的责任。6.1.3. 从专业分工来划分，DBA被分为生产DBA、应用程序DBA、过程和开发DBA。一些企业也会将数据存储系统从数据存储应用/架构独立出来，设有专门的网络存储管理员（NSA）负责。
      • 1.4.4. Database Architecture Types
        • 1. Centralized Databases 集中式数据库
          • Centralized databases have all the data in one system in one place. All users come to the one system to access the data.
            • For certain restricted data, centralization can be ideal, but for data that needs to be widely available, centralized databases have risks.
        • 2. Distributed Databases 分布式数据库
          • Distributed databases make possible quick access to data over a large number of nodes. Popular distributed database technologies are based on using commodity hardware servers. They are designed to scale out from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the database management software itself is designed to replicate data amongst the servers, thereby delivering a highly available service on top of a cluster of computers. Database management software is also designed to detect and handle failures. While any given computer may fail, the system overall is unlikely to.
          • federated (autonomous 自治的) or non-federated (non-autonomous 非自治的)
            • 
            • In contrast, a non-federated database system is an integration of component DBMS’s that are not autonomous; they are controlled, managed and governed by a centralized DBMS.
          • Federated Databases 联邦数据库
            • Federation provisions data without additional persistence or duplication of source data. A federated database system maps multiple autonomous database systems into a single federated database.
            • Federated databases are best for heterogeneous 异构 and distributed 分布式 integration 集成 projects such as enterprise information integration, data virtualization, schema matching, and Master Data Management.
            • A FDBMS can be categorized as either loosely or tightly coupled.
              • 
              • Loosely coupled systems require component databases to construct their own federated schema.
              • Tightly coupled systems consist of component systems that use independent processes to construct and publish an integrated federated schema,
          • Blockchain Database 区块链数据库
            • Blockchain databases are a type of federated database used to securely manage financial transactions.
            • There are two types of structures: individual records and blocks.
            • Hash algorithms 哈希算法 are used to create information about transactions to store in blocks while the block is the end of the chain.
        • 3. Virtualization / Cloud Platforms 可视化/云计算平台
          • Virtualization provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the service(s).
          • Cloud computing is a natural evolution of the widespread adoption of virtualization, service oriented architectures, and utility computing
            • Virtual machine image 虚拟机镜像
              • Cloud platforms allow users to purchase virtual machine instances for alimited time.
            • Database-as-a-service (DaaS) 数据库即服务
              • Some cloud platforms offer options for using a database-as-a-service,without physically launching a virtual machine instance for the database. In
            • Managed database hosting on the cloud 管理托管在云上的数据库
              • thecloud provider hosts the database and manages it on the application owner’s behalf.
          • DBA roles in Virtualization
            • Standardization/consolidation 标准化/整合
            • Server virtualization 服务器虚拟化
            • Automation 自动化
            • Security 安全
      • 1.4.5. Database Processing Types 数据处理类型
        • 1. ACID “酸”- 严谨
          • Atomicity 原子性
            • All operations are performed, or none of them is, so that if one part of the transaction fails,then the entire transaction fails.
          • Consistency 一致性
            • The transaction must meet all rules defined by the system at all times and must void half-completed transactions.
          • Isolation 隔离性
            • Each transaction is independent unto itself.
          • Durability 持久性
            • Once complete, the transaction cannot be undone.
        • 2. BASE “碱”- 灵活
          • 
          • The unprecedented increase in data volumes and variability, the need to document and store unstructured data, the need for read-optimized data workloads, and subsequent need for greater flexibility in scaling, design, processing, cost, and disaster recovery gave rise to the diametric opposite of ACID, appropriately termed BASE:
          • Basically Available 基本可用
            • The system guarantees some level of availability to the data even when there arenode failures. The data may be stale, but the system will still give and accept responses.
          • Soft State 软状态
            • The data is in a constant state of flux; while a response may be given, the data is notguaranteed to be current.
          • Eventual Consistency 最终一致性
            • The data will eventually be consistent through all nodes and in all databases,but not every transaction will be consistent at every moment.
          • 11. A search engine database is populated by a web crawler or spider software that usually processes as A：fundamentally available consistently true (FACT) B：linearly independent near exact (LINE) C：continually repeating, always performing (CRAP) D：atomic, consistent, isolated, durable (ACID) E：basically available ,soft state, eventually consistent(BASE) 正确答案：E 你的答案：E 解析：6.1.3.（2）BASE（保增长规模空前，数轻新增种类繁多，记录和存储结构化故据的需要，读优化和数据负载性能需要以及后续在横向扩展、设计、处理、成本及灾难恢复方面有更大灵活性的需要等.基本可用；软状态；最终一致
          • 23. The acronym BASE stands for : A：Basically Available，soft State，Eventual Consistency B：Basically Available，State Eventual Consistency C：Basically Available Soft Consistency，Eventual State D：Basically article，Soft Consistency，Eventual State E：Basically Article，Soft Consistency ,Eventual State 正确答案：A 你的答案：A 解析：6.1.3题解：（2）BASE数据增长规模空前，数据新增种类繁多。记录和存储非结构化数据的需要，读优化和数据负载性能需要以及后续在横向扩展、设计、处理、成本及灾难恢复方面有更大灵活性的需要等，基本可用；软状态；最终一致
        • 3. ACID vs BASE
          • 
        • 4. CAP 布鲁尔定理
          • The theorem asserts that a distributed system cannot comply with all parts of ACID at all time. The larger the system, the lower the compliance.
          • The CAP Theorem states that at most two of the three properties can exist in any shared-data system. This is usually stated with a ‘pick two’ statement, illustrated in Figure 58.
            • 
          • Consistency 一致性
            • The system must operate as designed and expected at all times.
          • Availability 可用性
            • The system must be available when requested and must respond to each request.
          • Partition Tolerance 分区容错
            • The system must be able to continue operations during occasions of data loss orpartial system failure.
      • 1.4.6. Data Storage Media 数据存储介质
        • 1. Disk and Storage Area Networks (SAN) 磁盘和存储区域网络
          • Disk arrays can be collected into Storage Area Networks (SAN). Data movement on a SAN may not require a network, as data can be moved on the backplane.
        • 2. In-Memory 内存
          • In-Memory databases (IMDB) 内存数据库
        • 3. Columnar Compression Solutions 列压缩方案
          • Columnar-based databases are designed to handle data sets in which data values are repeated to a great extent.
        • 4. Flash Memory 闪存
          • Flash memory combines the access speed of memory-based storage with the persistence of disk-based storage.
        • 5. Virtual Storage Area Network VSAN 虚拟存储区域网络
        • 6. Cloud-based storage solutions 云存储解决方案
        • 7. Radio Frequency Identification (RFID) 射频识别技术
        • 8. Digital wallets 数字钱包
        • 9. Data centers and Private, Public, and Hybrid Cloud Storage 数据中心与云存储
      • 1.4.7. Database Environments 数据库环境
        • 1. Production Environment 生产环境
          • The production environment is the technical environment where all business processes occur.
          • The production environment is the ‘real’ environment from a business perspective.
          • production environments should not be used for development and testing as these activities put production processes and data at risk.
        • 2. Pre-production Environments 非（预）生产环境
          • Pre-production environments are used to develop and test changes before such changes are introduced to the production environment. In order to detect potential issues, the configuration of pre-production environments must closely resemble the production environment.
          • include development, test, support, and special use environments
            • Development 开发环境
              • 1. The development environment should be the first place any patches or updates are applied for testing. This environment should be isolated from and on different physical hardware than the production environments.
              • 2. Do not move data from production environments without first determining what restrictions there are on doing so.
            • Test 测试环境
              • The test environment is used to execute quality assurance and user acceptance testing and, in some cases, stress or performance tests.
                • Quality Assurance Testing (QA): 质量保证测试
                • Integration Testing 集成测试
                • User Acceptance Testing (UAT): 用户验收测试
                • Performance Testing: 性能测试
              • Test environments should never write to production systems.
            • Sandboxes or Experimental Environments 数据沙盒和实验环境
              • A sandbox is an alternate environment that allows read-only connections to production data and can be managed by the users.
              • Sandboxes are used to experiment with development options and test hypotheses about data or merge production data with user-developed data or supplemental data obtained from external sources.
              • Sandboxes are valuable, for example, when performing a Proof-of-Concept.概念验证
              • 14. A sandbox is a type of database environment used for: A：user acceptance testing B：production backups C：proofs of concept and to test hypotheses D：low budget projects E：remote users 正确答案：C 你的答案：C 解析：6.1.3. 数据沙盒或实验环境数据沙盒是允许以只读的方式访问和管理生产数据的另一个环境。数据沙盒用于实验开发或者验证相关假设的数据，或者将用户自己开发的数据或从外部获得的补充数据与生产数据进行合并。数据沙盒的价值如同进行一场概念验证（Proof-of Concept，POC）。
      • 1.4.8. Database Organization 数据库组织模型
        • 
        • Data storage systems provide a way to encapsulate the instructions necessary to put data on disks and manage processing, so developers can simply use instructions to manipulate data. Databases are organized in three general ways: Hierarchical, Relational, and Non-Relational. These classes are not mutually exclusive (see Figure 59). Some database systems can read and write data organized in relational and non-relational structures. Hierarchical databases can be mapped to relational tables. Flat files with line delimiters can be read as tables with rows, and one or more columns can be defined to describe the row contents.
        • Hierarchical database 层次型数据库
          • Hierarchical database organization is the oldest database model, used in early mainframe DBMS, and is the most rigid of structures. In hierarchical databases, data is organized into a tree-like structure with mandatory parent/child relationships: each parent can have many children, but each child has only one parent (also known as a 1-to-many relationship).
          • Directory trees are an example of a hierarchy. XML also uses a hierarchical model
        • Relational database 关系型数据库
          • Relational databases are based on set theory 集合理论 and relational algebra 关系代数, where data elements or attributes (columns) are related into tuples (rows). (See Chapter 5.) Tables are sets of relations with identical structure. Set operations (like union, intersect, and minus) are used to organize and retrieve data from relational databases, in the form of Structured Query Language (SQL). In order to write data, the structure (schema) has to be known in advance (schema on write 写入时处理的模式). Relational databases are row-oriented.
          • The database management system (DBMS) of a relational database is called RDBMS. A relational database is the predominant choice in storing data that constantly changes. Variations on relational databases include Multidimensional and Temporal.
          • Multidimensional database 多维数据库
            • Multidimensional database technologies store data in a structure that allows searching using several data element filters simultaneously.
          • Temporal database 时态数据库
            • A temporal database is a relational database with built-in support for handling data involving time.
              • Valid time 有效时间 is the timeframe when a fact is true with respect to the entity it represents in the real world.
              • Transaction time 事务时间 is the period during which a fact stored in the database is considered true.
            • It is possible to have timelines other than Valid Time and Transaction Time, such as Decision Time, in the database. In that case, the database is called a multi-temporal database 多时态数据库 as opposed to a bi-temporal database.双时态数据库
        • Non-relational database 非关系型数据库
          • 1. Non-relational databases can store data as simple strings or complete files. referred to as ‘schema on read’ 读出时进行处理的模式
          • 2. Non-relational databases are usually referred to as NoSQL (which stands for “Not Only SQL”). The primary differentiating factor is the storage structure itself, where the data structure is no longer bound to a tabular relational design. It could be a tree, a graph, a network, or a key-value pairing.
            • 24. The acronym NoSQL stands for: A：Not SQL B：Not Only SQL C：NO SQL D：None Answer E：all 正确答案：B 你的答案：A 解析：6.1.3. 非关系型数据库通常被称为NoSQL数据库（代表“Not Only SQL"）。主要区别是存储结构，数据结构不再绑定到表格关系中设计。它可能是树、图、网络或键值对。
          • 3. These databases are often highly optimized data stores intended for simple retrieval and appending operations. The goal is improved performance, especially with respect to latency and throughput. NoSQL databases are used increasingly in Big Data and real-time web applications.
          • 4. Column-oriented database 列式数据库
            • Column-oriented databases are used mostly in Business Intelligence applications because they can compress redundant data.
            • There are trade-offs between column-oriented (non-relational) and row-oriented (usually relational) organization.
              • 1. Column-oriented organization is more efficient when an aggregate needs to be computed over manyrows. This only holds true for a notably smaller subset of all columns of data, because reading thatsmaller subset of data can be faster than reading all data.
              • 2. Column-oriented organization is more efficient when new values of a column are supplied for all rowsat once, because that column data can be written efficiently to replace old column data withouttouching any other columns for the rows.
              • 3. Row-oriented organization is more efficient when many columns of a single row are required at thesame time, and when row-size is relatively small, as the entire row can be retrieved with a single diskseek.
              • 4. Row-oriented organization is more efficient when writing a new row if all of the row data is suppliedat the same time; the entire row can be written with a single disk seek.
              • 5. In practice, row-oriented storage layouts are well suited for Online Transaction Processing (OLTP)-like workloads, which are more heavily loaded with interactive transactions. Column-oriented storage layouts are well suited for Online Analytical Processing (OLAP)-like workloads
          • 5. Spatial database 空间数据库
            • A spatial database is optimized to store and query data that represents objects defined in a geometric space.
            • Spatial databases support several primitive types (simple geometric shapes such as box, rectangle, cube, cylinder, etc.) and geometries composed of collections of points, lines, and shapes.
            • Spatial databases can perform a wide variety of spatial operations. As per the Open Geospatial Consortium standard, a spatial database may perform one or more of the following operations:
              • Spatial Measurements 空间评估
                • Computes line length, polygon area, the distance between geometries, etc.
              • Spatial Functions 空间功能
                • Modifies existing features to create new ones
              • Spatial Predicates 空间预测
                • Allows true/false queries about spatial relationships between geometries.
              • Geometry Constructors 几何构造
                • Creates new geometries, usually by specifying the vertices 顶点 (points or nodes)which define the shape.
              • Observer Functions 观测功能
                • Queries that return specific information about a feature such as the location of the center of a circle.
          • 6. Object / Multi-media database 对象/多媒体数据库
            • A multimedia database includes a Hierarchical Storage Management system for the efficient management of a hierarchy of magnetic 磁 and optical 光 storage media.
          • 7. Flat File Database 平面文件数据库
            • A flat file database describes any of various means to encode a data set as a single file.
            • Strictly, a flat file database consists of nothing but data, and contains records that may vary in length and delimiters.
            • A flat file can be a plain text file or a binary file.
            • Flat files are used not only as data storage tools in DBMS systems, but also as data transfer tools. Hadoop databases use flat file storage.
          • 8. Key-Value Pair database 键值对数据库
            • Key-Value pair databases contain sets of two items: a key identifier and a value.
              • Document Databases 文档数据库
                • Document-oriented databases contain collections of files including bothstructure and data
                • Document databases may use XML or JSON (Java ScriptObject Notation) structures.
              • Graph Databases 图数据库
                • Graph databases store key-value pairs where the focus is on the relationshipbetween the nodes, rather than on the nodes themselves.
          • 9. Triplestore 三元组存储
            • A data entity composed of subject-predicate-object 主-谓-宾 is known as a triplestore.
            • Triplestore databases are best for taxonomy 分类 and thesaurus 同义词 management, linked data integration, and knowledge portals.
            • three categories
              • Native triplestores 原生三元组存储 are those that are implemented from scratch and exploit the RDF data model to efficiently store and access the RDF data.
              • RDBMS-backed triplestores RDBMS支持的三元组存储 are built by adding an RDF specific layer to an existing RDBMS.
              • NoSQL Triplestores are currently being investigated as possible storage managers for RDF.
      • 1.4.9. Specialized Databases 专用数据库
        • Computer Assisted Design and Manufacturing (CAD / CAM) 计算机辅助设计和制造 applications require an Object database, as will most embedded real-time applications.
        • Geographical Information Systems (GIS) 地理信息系统 make use of specialized geospatial databases, which have at least annual updates to their Reference Data. Some specialized GIS are used for utilities (electricgrid, gas lines, etc.), for telecom in network management, or for ocean navigation.
        • Shopping-cart 购物车 applications found on most online retail websites, make use of XML databases toinitially store the customer order data, and may be used real-time by social media databases for adplacement on other websites.
        • In addition, many off-the-shelf vendor applications may use their own proprietary databases. At the very least, their schemas will be proprietary and mostly concealed, even if they sit on top of traditional relational DBMSs.
      • 1.4.10. Common Database Processes 常见数据库过程
        • 1. Archiving 数据归档
          • Archiving is the process of moving data off immediately accessible storage media and onto media with lower retrieval performance. Archives can be restored to the originating system for short-term use. Data that is not actively needed to support application processes should be moved to an archive on less-expensive disk, tape, or a CD / DVD jukebox. Restoring from an archive should be a matter of simply copying the data from the archive back into the system.
            • 15. Data that is used infrequently or not at all may be moved to an alternative data store. This is called A：analysis B：replication C：archiving D：auditing E：authentication 正确答案：C 你的答案：C 解析：6.1.3. 归档过程必须与分区策略保持一致，以确保最佳的可用性和数据保留度。稳妥的方法包括：3）将不经常使用的数据复制到单独的数据库。
            • 12. Periodic archiving of transaction data from a production CRM system is critical for : A：managing deleted customer records B：enabling the distribution of transaction data across the enterprise C：training junior DBAs D：the maintenance of database performance E：providing alternate sources for reporting systems 正确答案：D 你的答案：D 解析：6.1.3. 归档过程必须与分区策略保持一致，以确保最佳的可用性和数据保留度。稳妥的方法包括：3）将不经常使用的数据复制到单独的数据库。
          • It is wise to schedule regular tests of archive restoration to ensure avoid surprises in an emergency
          • A robust 稳妥的 approach involves:
            • 1. Creating a secondary storage area, preferably on a secondary database server
            • 2. Partitioning existing database tables into archival blocks
            • 3. Replicating the data that is needed less often to the separate database
            • 4. Creating tape or disk backups
            • 5. Creating database jobs that periodically purge unneeded data
          • There are several ways of handling out-of-synch archives:
            • 1. Determine if or how much of the archive is required to be preserved. What is not required can beconsidered purged.
            • 2. For major changes in technology, restore the archives to the originating system before the technologychange, upgrade or migrate to the new technology, and re-archive the data using the new technology.
            • 3. For high-value archives where the source database structures change, restore the archive, make anychanges to the data structures, and re-archive the data with the new structure.
            • 4. For infrequent-access archives where the source technology or structure changes, keep a small versionof the old system running with limited access, and extract from the archives using the old system asneeded.
        • 2. Capacity and Growth Projections 容量增长预测
          • 13. A database that is growing at 100% per annum compound will be A：6 times its original size at the end of year. B：3 times its original size at the end of year 2 C：half its original size at the end of year 2. D：2 times its original size at the end of year 5 E：4 times its original size at the end of year 2. 正确答案：E 你的答案：E 解析：正确答案：E相关章节：6.1.3题解：例如，原始为k，第一年结束2k，第二年结束4k（2）容量和增长预测把数据库想象成一个盒子，把数据想象成水果，把管理成本（索引等）想象成包装材料。用隔板把盒子隔成小格子，将水果和包装材料放进各个小格子：1）先确定盒子的大小。它要容纳所有的水果和必需的包装材料，这就是容量（Capacity）。2）有多少水果要放进盒子，放的速度有多快？3）有多少水果要取出盒子，取的速度有多快？确定盒子的容量是随着时间的推移保持不变，还是必须随着时间的推移而扩大，以便确定存放更多的水果。对盒子要装进来的水果和包装材料的多少和多快所作出的预测，就称为增长预测（Growth Projection）。如果盒子不能扩大，那么水果必须尽可能从盒子里快进快出，增长预测即为零。
        • 3. Change Data Capture (CDC) 变动数据捕获
          • Change data capture refers to the process of detecting that data has changed and ensuring that information relevant to the change is stored appropriately.
          • Rather than sending the entire database over the network to reflect just a few minor changes, the idea is to just send what changed (deltas), so that the receiving system can make appropriate updates.
            • 25. A Data Integration approach that updates a data Warehouse with small changes from Operational systems is called A：CDC 变更数据捕获 B：ETL 抽取转换加载 C：SOA 面向服务架构 D：EII 企业信息集成 E：ELT 正确答案：A 你的答案：A 解析：6.1.3：（3）变动数据捕获变动数据捕获（Change Data Capture，CDC）是指检测到数据的变动并确保与变动相关的信息被适当记录的过程。
          • There are two different methods to detect and collect changes:
            • data versioning 数据版本控制
              • which evaluates columns that identify rows that have changed (e.g., last-update-timestamp columns, version-number columns, status-indicator columns),
            • by reading logs 日志读取
              • that document the changes and enable them to be replicated in secondary systems.
        • 4. Purging 数据清除
          • Purging is the process of completely removing data from storage media such that it cannot be recovered.
            • 1. Eventually, the data will fill the available space, and performance will begin to degrade. At that point, data will need to be archived, purged, or both.
            • 2. Just as importantly, some data will degrade in value and is not worth keeping. Purging is the process of completely removing data from storage media such that it cannot be recovered. A principal goal of data management is that the cost of maintaining data should not exceed its value to the organization. Purging data reduces costs and risks.
            • 3. Data to be purged is generally deemed obsolete and unnecessary, even for regulatory purposes. Some data may become a liability if kept longer than necessary. Purging it reduces the risks that it may be misused.
        • 5. Replication 数据复制
          • Data replication means same data is stored on multiple storage devices.
            • Active replication 主动复制
              • is performed by recreating and storing the same data at every replica from every other replica.
            • Passive replication 被动复制
              • involves recreating and storing data on a single primary replica and thentransforming its resultant state to other secondary replicas
          • In some situations, having duplicate databases is useful, such as in a high-availability environment where spreading the workload among identical databases in different hardware or even data centers can preserve functionality during peak usage times or disasters.
          • Replication has two dimensions of scaling:
            • Horizontal data scaling 水平扩展 has more data replicas.
            • Vertical data scaling 垂直扩展 has data replicas located further away in distance geographically.
          • Replication transparency occurs when data is replicated between database servers so that the information remains consistent throughout the database system and users cannot tell or even know which database copy they are using.
          • The two primary replication patterns 两种主要方法
            • 
            • In mirroring 镜像, updates to the primary database are replicated immediately (relatively speaking) to the secondary database, as part of a two-phase commit process.
            • In log shipping 日志传送, a secondary server receives and applies copies of the primary database’s transactionlogs at regular intervals.
        • 6. Resiliency and Recovery 韧性与恢复
          • Resiliency 韧性 in databases is the measurement of how tolerant a system is to error conditions. If a system can tolerate a high level of processing errors and still function as expected, it is highly resilient. If an application crashes upon the first unexpected condition, that system is not resilient. If the database can detect and either abort or automatically recover from common processing errors (runaway query, for example), it is considered resilient. There are always some conditions that no system can detect in advance, such as a power failure, and those conditions are considered disasters.
          • Three recovery types
            • Immediate recovery 立即恢复 from some issues sometimes can be resolved through design; for example,predicting and automatically resolving issues, such as those that might be caused by a failover tobackup system.
            • Critical recovery 关键恢复 refers to a plan to restore the system as quickly as possible in order to minimize delays or shut downs of business processes.
            • Non-critical recovery 非关键恢复 means that restoration of function can be delayed until systems that are morecritical have been restored.
        • 7. Retention 数据保留
          • Data Retention refers to how long data is kept available. Data retention planning should be part of the physical database design. Retention requirements also affect capacity planning.
            • Data Security
              • Failure to retain data for the appropriate length of time can have legal consequences. Likewise, there are also regulations related to purging data. Data can become a liability if kept longer than specified.
            • legal reasons.
              • Organizations should formulate retention policies based on regulatory requirements and risk management guidelines. These policies should drive specifications for purging and archiving of data.
              • 9. Discuss __ with the data owners at design time and reach policy agreement. A：materialized views 物化视图 B：data retention C：hyperbolic indexing 双曲分度 D：enterprise resource planning (ERP) E：Latency 延迟 正确答案：B 你的答案：B 解析：6.1.3.（7）数据保留数据保留（Retention）是指数据保持可用的时间。数据保留规划应该是物理数据库设计的一部分。数据保留需求也会影响容量规划。
        • 8. Sharding 数据分片
          • Sharding is a process where small chunks of the database are isolated and can be updated independently of other shards, so replication is merely a file copy. Because the shards are small, refreshes/overwrites may be optimal.
  • 2. Activities
    • 2.1. Manage Database Technology - Database Technology Support 数据库技术支持
      • 2.1.1. Understand Database Technology Characteristics
        • Data professionals must first understand the characteristics of a candidate database technology before determining which to recommend as a solution.
        • Do not assume that a single type of database architecture or DBMS works for every need.
      • 2.1.2. Evaluate Database Technology
        • Product factors
          • 1. Product architecture and complexity
          • 2. Volume and velocity 速度 limits, including streaming rate
          • 3. Application profile, such as transaction processing, Business Intelligence, and personal profiles
          • 4. Specific functionality, such as temporal calculation 时间计算 support
          • 5. Hardware platform and operating system support
          • 6. Availability of supporting software tools
          • 7. Performance benchmarks, including real-time statistics
          • 8. Scalability 可扩展性
          • 9. Software, memory, and storage requirements
          • 10. Resiliency 韧性, including error handling and reporting
        • Org and Vendors factors
          • 1. Organizational appetite for technical risk
          • 2. Available supply of trained technical professionals
          • 3. Cost of ownership, such as licensing, maintenance, and computing resources
          • 4. Vendor reputation
          • 5. Vendor support policy and release schedule
          • 6. Customer references
        • It is a good idea to start with a small pilot project or a proof-of-concept (POC), to get a good idea of the true costs and benefits before proceeding with a full-blown production implementation.
      • 2.1.3. Manage and Monitor Database Technology
        • DBAs should have working knowledge of application development skills, such as data modeling, use-case analysis, and application data access.
          • 3. The following are critical success factors for information strategy planning EXCEPT A：participation of enterprise's functional representatives 职能代表 B：committed corporate steering committee. C：use case analysis 用例分析 D：structured process E：all 正确答案：C 你的答案：A 解析：6.2.1. DBA应具备应用程序开发的相关知识，如数据建模、用例分析和应用程序的数据
    • 2.2. Manage Databases - Database Operations Support. 数据操作支持
      • 1. Database support, as provided by DBAs and Network Storage Administrators (NSAs), is at the heart of data management.
        • DBAs manage various data storage applications by assigning storage structures, maintaining physical databases (including physical data models and physical layouts of the data, such as assignments to specific files or disk areas), and establishing DBMS environments on servers.
      • 2. Understand Requirements
        • 1. Define Storage Requirements
          • Doing any sort of maintenance in an emergency is a risk.
          • All projects should have an initial capacity estimate for the first year of operations, and a growth projection for the following few years. Capacity and growth should be estimated not only for the space the data itself holds, but also for indexes, logs, and any redundant images such as mirrors.
          • Data storage requirements must account for regulation related to data retention. For legal reasons, organizations are required to retain some data for set periods (see Chapter 9). In some cases, they may also be required to purge data after a defined period. It’s a good idea to discuss data retention needs with the data owners at design time and reach agreement on how to treat data through its lifecycle.
            • 8. Although cloud solutions promote the ability to be ‘mundane and opaque 平凡且不透明'，for data governance，it is necessary to understand where data is located to A：tune systems performance 调优 B：meet green objectives C：address compliance requirements D：manage service levels E：Data Asset valuation 估值 正确答案：C 你的答案：C 解析：6.2.2.数据存储需求必须考虑与数据保留相关的法规。出于合规考虑，组织需要保留特定时间周期的一些数据（参见第9章）。在某些情况下，过了规定的时间周期，他们还要求清除数据。最好在设计时与数据所有者讨论数据的保留需求，并就在数据的整个生命周期中如何处理数据达成一致。
        • 2. Identify Usage Patterns 识别使用模式
          • Transaction-based
          • Large data set write- or retrieval-based 基于大数据集的读写型
          • Time-based
          • Location-based
          • Priority-based
        • 3. Define Access Requirements
      • 3. Plan for Business Continuity
        • Organizations need to plan for business continuity in the event of disaster or adverse event that impacts their systems and their ability to use their data. DBAs must make sure a recovery plan exists for all databases and database servers, covering scenarios that could result in loss or corruption of data, such as:
          • 1. Loss of the physical database server
          • 2. Loss of one or more disk storage devices
          • 3. Loss of a database, including the DBMS Master Database, temporary storage database, transaction logsegment, etc.
          • 4. Corruption of database index or data pages
          • 5. Loss of the database or log segment filesystems
          • 6. Loss of database or transaction log backup files
        • Management and the organization’s business continuity group, if one exists, should review and approve the data recovery plan.
        • The DBA group should regularly review the plans for accuracy and comprehensiveness
          • 4. A database continuity plan should be written in accordance 复合 with the business recovery （） A：procedures B：team members C：goals D：authorizations 授权 E：all 正确答案：C 你的答案：C 解析：符合+目标更合理。6.2.2.规划业务连续性组织需要为灾难事件、影响系统或影响使用数据的不利事件进行业务连续性规划（Plan for Business Continuity）。DBA必须确保所有数据库和数据库服务器都有恢复计划，包括可能导致数据丢失或数据损坏的场景，
        • Make Backups 备份数据
          • Make backups of databases and, if appropriate, the database transaction logs. The system’s Service Level Agreement (SLA) should specify backup frequency.
          • Backup files should be kept on a separate file system from the databases, and should be backed up to some separate storage medium as specified in the SLA.
        • Recover Data 恢复数据
          • It is critical to periodically 定期的 test recovery of data. Doing so will reduce bad surprises during a disaster or emergency.
            • 16. The best way to validate that a database backup is working, is to A：check the size of the backup file. B：appoint a special DBA in charge of backups C：check the backup logs every day. D：periodically recover from the backup file E：check for the automatic email notification of backup success 正确答案：D 你的答案：D 解析：6.2.2. 规划业务连续性的备份和恢复数据：定期进行数据库的恢复测试是非常重要的。
          • Practice runs can be executed on non-production system copies with identical infrastructure and configuration, or if the system has a failover, on the secondary system.
      • 4. Develop Database Instances 创建数据库实例
        • DBAs are responsible for the creation of database instances. Related activities include:
          • 1. Installing and updating DBMS software
          • 2. Maintaining multiple environment installations, including different DBMS versions
            • sandbox 沙盒环境
            • development 开发
            • testing 测试
            • user acceptance testing UAT
            • system acceptance testing 系统验收测试
            • quality assurance 质量保证
            • pre-production 预生产
            • hot-fix 热修复
            • disaster recovery environments 灾难恢复
            • production 生产环境
          • 3. Installing and administering related data technology
        • Steps
          • 1. Manage the Physical Storage Environment
            • Software Configuration Management (SCM) 软件配置管理步骤
              • Configuration identification 配置识别
                • DBAs will work with data stewards, data architects,and data modelers to identify the attributes that define every aspect of a configuration for end-userpurposes. These attributes are recorded in configuration documentation and baselined. Once anattribute is baselined a formal configuration change control processes is required to change theattribute.
              • Configuration change control 配置变更控制
                • is a set of processes and approval stages required to change aconfiguration item’s attributes and to re-baseline them.
              • Configuration status accounting 配置状态报告
                • is the ability to record and report on the configuration baselineassociated with each configuration item at any point in time.
              • Configuration audits 配置审计
                • occur both at delivery and when effecting a change. There are two types. Aphysical configuration audit ensures that a configuration item is installed in accordance with therequirements of its detailed design documentation, while a functional configuration audit ensures thatperformance attributes of a configuration item are achieved.
          • 2. Manage Database Access Controls
            • DBAs oversee the following functions to protect data assets and data integrity
              • Controlled environment 受控环境
                • DBAs work with NSAs to manage a controlled environment for data assets;this includes network roles and permissions management, 24x7 monitoring and network healthmonitoring, firewall management, patch management, and Microsoft Baseline Security Analyzer(MBSA) integration.
              • Physical security 物理安全
                • The physical security of data assets is managed by Simple Network ManagementProtocol (SNMP)-based monitoring, data audit logging, disaster management, and database backupplanning. DBAs configure and monitor these protocols. Monitoring is especially important for securityprotocols.
              • Monitoring 监控
                • Database systems are made available by continuous hardware and software monitoring ofcritical servers.
              • Controls 控制
                • DBAs maintain information security by access controls, database auditing, intrusiondetection, and vulnerability assessment tools.
          • 3. Create Storage Containers
            • All data must be stored on a physical drive
          • 4. Implement Physical Data Models
          • 5. Load Data
          • 6. Manage Data Replication
            • DBAs can influence decisions about the data replication process by advising on:
              • Active or passive replication
              • Distributed concurrency control from distributed data systems
              • The appropriate methods to identify updates to data through either timestamp or version numbersunder Change Data Control process
      • 5. Manage Database Performance
        • The Database performance depends on two interdependent facets: availability 可用性 and speed 响应速度.
          • Performance includes ensuring availability of space, query optimization, and other factors that enable a database to return data in an efficient way. Performance cannot be measured without availability. An unavailable database has a performance measure of zero.
          • 6. which of the following is the MOST important factor affecting the response time for populating a well-designed relational database? A：Frequency of data access B：File format of existing data C：Complexity of program logic D：Systems integration E：All 正确答案：C 你的答案：B 解析：因为数据库设计良好，那么就是程序写的不好造成的问题.相关章节：6.2.2 4.管理数据库性能数据库的性能取决于两个相互依赖的因素：可用性和响应速度。性能包括确保空间的可用性、查询优化以及其他能使数据库以有效的方式返回数据的因素。如果没有可用性，就无法衡量数据库的性能。一个不可用的数据库其性能指标为0。
          • 19. Database performance depends upon two independent facets. These are A：distance to data center and network bandwidth B：choice of DBMs and programming language C：number of users and number of tables. D：availability and speed E：hardware and network. 正确答案：D 你的答案：D 解析：6.2.2.4管理数据库性能取决于两个相互依赖的因素，可用性和响应速度
        • DBAs and NSAs manage database performance by:
          • 1. Setting and tuning 优化 operating system and application parameters.
          • 2. Managing database connectivity. 管理数据库连接
          • 3. Working with system programmers and network administrators to tune operating systems, networks,and transaction processing middleware to work with the database.
          • 4. Dedicating appropriate storage and enabling the database to work with storage devices and storage management software.
          • 5. Providing volumetric growth studies 容量增长预期 to support storage acquisition and general data lifecycle management activities of retention, tuning, archiving, backup, purging 清理, and disaster recovery.
          • 6. Working with system administrators to provide operating workloads and benchmarks of deployed dataassets that support SLA management, charge-back calculations, server capacity, and lifecycle rotationwithin the prescribed planning horizon.
        • Steps
          • 1. Set Database Performance Service Levels
            • 
            • System performance, data availability and recovery expectations, and expectations for teams to respond to issues are usually governed through Service Level Agreements (SLAs) between IT data management services organizations and data owners
            • Typically, an SLA will identify the timeframes during which the database is expected to be available for use. Often an SLA will identify a specified maximum allowable execution time for a few application transactions (a mix of complex queries and updates). If the database is not available as agreed to, or if process execution times violate the SLA, the data owners will ask the DBA to identify and remediate the causes of the problem.
          • 2. Manage Database Availability
            • Availability is the percentage of time that a system or database can be used for productive work. As organizations increase their uses of data, availability requirements increase, as do the risks and costs of unavailable data. To meet higher demand, maintenance windows are shrinking.
            • Four related factors affect availability:
              • 1. Manageability 可管理性
                • The ability to create and maintain an environment
              • 2. Recoverability 可恢复性
                • The ability to reestablish service after interruption, and correct errors caused byunforeseen events or component failures
              • 3. Reliability 可靠性
                • The ability to deliver service at specified levels for a stated period
              • 4. Serviceability 可维护性
                • The ability to identify the existence of problems, diagnose their causes, and repair /solve them
            • Impact factors
              • 1. Planned outages 计划性停机
                • For maintenance
                • For upgrades
              • 2. Unplanned outages 非计划性停机
                • Loss of the server hardware
                • Disk hardware failure
                • Operating system failure
                • DBMS software failure
                • Data center site loss
                • Network failure
              • 3. Application problems 应用问题
                • Security and authorization problems
                • Severe performance problems
                • Recovery failures
              • 4. Data problems 数据问题
                • Corruption of data (due to bugs, poor design, or user error)
                • Loss of database objects
                • Loss of data
                • Data replication failure
              • 5. Human error 人为错误
            • DBAs are responsible for doing everything possible to ensure databases stay online and operational, including:
              • 1. Running database backup utilities
              • 2. Running database reorganization utilities
              • 3. Running statistics gathering utilities
              • 4. Running integrity checking utilities
              • 5. Automating the execution of these utilities
              • 6. Exploiting table space clustering and partitioning
              • 7. Replicating data across mirror databases to ensure high availability
          • 3. Manage Database Execution
            • DBAs also establish and monitor database execution, use of data change logs, and synchronization of duplicated environments. Log sizes and locations require space and in some cases can be treated like file-based databases on their own.
            • Other applications that consume logs must also be managed, to ensure use of the correct logs at the required logging level. The more detail that is logged, the more space and processing required, which may adversely affect performance.
          • 4. Maintain Database Performance Service Levels
            • DBAs optimize database performance both proactively and reactively, by monitoring performance and by responding to problems quickly and competently.
              • Transaction Performance vs. Batch Performance
                • Data movement may occur in real time through online transactions. However, many data movement and transformation activities are performed through batch programs,
              • Issue Remediation
                • When performance problems occur, the DBA, NSA, and Server Administration teams should use the monitoring and administration tools of the DBMS to help identify the source of the problem.
                • Common reasons for poor database performance include:
                  • 1. Memory allocation or contention 内存分配和征用
                    • A buffer or cache for data.
                  • 2. Locking and blocking 锁和阻塞
                    • In some cases, a process running in the database may lock up databaseresources, such as tables or data pages, and block another process that needs them.
                  • 3. Inaccurate database statistics
                    • Most relational DBMSs have a built-in query optimizer, which relieson stored statistics about the data and indexes to make decisions about how to execute a given querymost effectively. These statistics should be updated frequently, especially in active databases. Failureto do so will result in poorly performing querie
                  • 4. Poor coding 不良代码
                    • Perhaps the most common cause of poor database performance is poorly coded SQL.
                  • 5. Inefficient complex table joins 低效复杂的表连接
                    • Use views to pre-define complex table joins.
                  • 6. Insufficient indexing 不当索引
                    • Be careful about creating too manyindexes on heavily updated tables, as this will slow down update processing.
                  • 7. Application activity
                    • Ideally, applications should be running on a server separate from the DBMS, sothat they are not competing for resources.
                  • 8. Overloaded servers
                    • For DBMSs that support multiple databases and applications, there may be abreaking point where the addition of more databases has an adverse effect on the performance ofexisting databases.
                  • 9. Database volatility 数据库易变性
                    • In some cases, large numbers of table inserts and deletes over a short while cancreate inaccurate database distribution statistics.
                  • 10. Runaway queries 失控的查询语句
                    • Users may unintentionally submit queries that use a majority of the system’sshared resources.
                • After the cause of the problem is identified, the DBA will take whatever action is needed to resolve the problem,
                  • working with application developers to improve and optimize the database code
                  • archiving or deleting data that is no longer actively needed by application processes
                  • In exceptional cases for OLTP-type databases, the DBA may consider working with the data modeler restructure the affected portion of the database.
                  • For read-only reporting and analytical databases, denormalization for performance and ease of access is the rule rather than the exception, and poses no threat or risk
          • 5. Maintain Alternate Environments
            • Databases do not appear once and remain unchanged. Business rules change, business processes change, and technology changes. Development and test environments enable changes to be tested before they are brought into a production environment. DBAs can make whole or subset copies of database structures and data onto other environments to enable development and testing of system changes.
            • There are several types of alternate environments.
              • 1. Development environments
                • are used to create and test changes that will be implemented inproduction. Development must be maintained to closely resemble the production environment, thoughwith scaled down resources.
              • 2. Test environments
                • serve several purposes: QA, integration testing, UAT, and performance testing.The test environment ideally also has the same software and hardware as production. In particular,environments used for performance testing should not be scaled down in resources.
              • 3. Sandboxes or experimental environments are used to test hypotheses and develop new uses of data
                • The DBAs generally set up, grant access to, and monitor usage of these environments. They shouldalso ensure that sandboxes are isolated and do not adversely affecting production operations
              • 4. Alternate production environments
                • are required to support offline backups, failover, and resiliencysupport systems. These systems should be identical to the production systems, although the backup(and recovery) system can be scaled down in compute capacity, since it is mostly dedicated to I/Oactivities.
      • 6. Manage Test Data Sets
        • Software testing is labor-intensive and accounts for nearly half of the cost of the system development.
        • Test data is data that has been specifically identified to test a system.
        • Test data may be produced in a focused or systematic way
        • Test data is data that has been specifically identified to test a system. Testing can include verifying that a given set of input produces expected output or challenging the ability of programming to respond to unusual, extreme, exceptional, or unexpected input.
          • Test data can be completely fabricated or generated using meaningless values or it can be sample data. Sample data can be a subset of actual production data (by either content or structure), or generated from production data
        • In cases where production data contains protected or restricted data, sample data must be masked.
      • 7. Manage Data Migration
        • Data migration is the process of transferring data between storage types, formats, or computer systems, with as little change as possible
        • Data migration is a key consideration for any system implementation, upgrade, or consolidation. It is usually performed programmatically, being automated based on rules.
        • migration techniques:
          • Moving data off an over-used storage device to a separate environment
          • Moving data onto a faster storage device as needs require
          • Implementing an Information Lifecycle Management policy
          • Migrating data off older storage devices (either being scrapped or off-lease) to offline or cloud storage
        • Automated and manual data remediation is commonly performed in migration to improve the quality of data, eliminate redundant or obsolete information, and match the requirements of the new system. Data migration phases (design, extraction, remediation, load, verification) for applications of moderate to high complexity are commonly repeated several times before the new system is deployed.
        • The mapping granularity dictates how quickly the Metadata can be updated, how much extra capacity is required during the migration, and how quickly the previous location is marked as free. Smaller granularity means faster update, less space required, and quicker freeing up of old storage.
  • 3. Tools
    • 3.1. Data Modeling Tools
      • 3.1.1. Some data modeling tools allow the generation of database data definition language (DDL).
      • 3.1.2. Most support reverse engineering from database into a data model.
    • 3.2. Database Monitoring Tools
      • 3.2.1. Database monitoring tools automate monitoring of key metrics, such as capacity, availability, cache performance, user statistics, etc., and alert DBAs and NSAs to database issues. Most such tools can simultaneously monitor multiple database types.
    • 3.3. Database Management Tools
      • 3.3.1. Database systems have often included management tools. In addition, several third-party software packages allow DBAs to manage multiple databases.
    • 3.4. Developer Support Tools
      • 3.4.1. Developer Support tools contain a visual interface for connecting to and executing commands on a database. Some are included with the database management software. Others include third-party applications.
  • 4. Techniques
    • 4.1. Test in Lower Environments 在低阶环境中测试
      • 4.1.1. For upgrades and patches to operating systems, database software, database changes, and code changes, install and test on the lowest level environment first – usually development.
    • 4.2. Physical Naming Standards 物理命名标准
      • 4.2.1. ISO/IEC 11179 – Metadata registries (MDR)
    • 4.3. Script Usage for All Changes 所有变更操作脚本化
      • 4.3.1. It is extremely risky to directly change data in a database.
      • 4.3.2. It is helpful to place changes to be made into update script files and test them thoroughly in non-production environments before applying to production.
  • 5. Implementation Guidelines
    • 5.1. Readiness Assessment / Risk Assessment 就绪评估和风险评估
      • 5.1.1. Data loss 数据丢失风险:
        • Data can be lost through technical or procedural errors, or through malicious intent. 恶意的目的
        • SLAs need to be supported by well-documentedprocedures.
      • 5.1.2. Technology readiness 技术准备风险
        • Newer technologies such as NoSQL, Big Data, triple stores, and FDMSrequire skills and experience readiness in IT.
    • 5.2. Organization and Cultural Change
      • 5.2.1. DBAs need to recognize the legitimate concerns of data owners and data consumers, balance short-term and long-term data needs DBAs risk propagating an ‘us versus them’ mentality, and being regarded as dogmatic, impractical, unhelpful, and obstructionist.
      • 5.2.2. Organizations generally regard information technology in terms of specific applications, not data,
      • 5.2.3. Application development often sees data management as an impediment to application development, as something that makes development projects take longer and cost more without providing additional benefit.
      • 5.2.4. DBAs and other data management practitioners should:
        • Proactively communicate 主动沟通
        • Communicate with people on their level and in their terms 换位思考
        • Stay business-focused 专注业务
        • Be helpful 对他人有帮助
        • Learn continually 不断学习
  • 6. Data Storage and Operations Governance
    • 6.1. Metrics
      • 6.1.1. Data Storage metrics
        • 1. Count of databases by type
        • 2. Aggregated transaction statistics 汇总交易统计
        • 3. Capacity metrics, such as
          • Amount of storage used
          • Number of storage containers
          • Number of data objects in terms of committed and uncommitted block or pages
          • Data in queue 数据队列
        • 4. Storage service usage
        • 5. Requests made against the storage services
        • 6. Improvements to performance of the applications that use a service
      • 6.1.2. Performance metrics
        • 1. Transaction frequency and quantity
        • 2. Query performance
        • 3. API (application programming interface) service performance
      • 6.1.3. Operational metrics
        • 1. Aggregated statistics about data retrieval time 数据检索时间
        • 2. Backup size
        • 3. Data quality measurement
        • 4. Availability
      • 6.1.4. Service metrics
        • 1. Issue submission, resolution, and escalation count by type
        • 2. Issue resolution time
      • 6.1.5. DBAs need to discuss the need for metrics with data architects, Data Quality teams.
    • 6.2. Information Asset Tracking
      • 6.2.1. Part of data storage governance includes ensuring that an organization complies with all licensing agreements and regulatory requirements. Carefully track and conduct yearly audits of software license and annual support costs, as well as server lease agreements and other fixed costs. Being out of compliance with licensing agreements poses serious financial and legal risks for an organization.
      • 6.2.2. Audit data can help determine the total cost-of-ownership (TCO) for each type of technology and technology product. Regularly evaluate technologies and products that are becoming obsolete, unsupported, less useful, or too expensive.
    • 6.3. Data Audits and Data Validation
      • 6.3.1. Data audit is performed to investigate specific concerns about a data set and is designed to determine whether the data was stored in compliance with contractual and methodological requirements.
      • 6.3.2. Data validation is the process of evaluating stored data against established acceptance criteria to determine its quality and usability. Data validation procedures depend on the criteria established by the Data Quality team (if one is in place) or other data consumer requirements.
        • 1. 2. The information quality characteristic that measures the degree of conformance 一致性 of data to its DO main values and business rules is called A：validity B：precision C：completeness D：accuracy E：Accuracy 正确答案：A 你的答案：B 解析：6.6.3数据审计与数据有效性数据审计是根据定义的标准对数据集进行评估的过程，通常是对数据集的特定关注点进行审计。审计的目的是为了确定数据的存储是否符合合同和方法要求。数据审计方法可能包括一个项目特定和全面的检查表、所需的可交付成果和质量控制标准。数据验证是根据既定的验收标准评估存储数据的过程，以确定保质量和可用性。
      • 6.3.3. DBAs support part of data audits and validation by:
        • 1. Helping develop and review the approach
        • 2. Performing preliminary data screening and review
        • 3. Developing data monitoring methods
        • 4. Applying statistical, geo-statistical, and bio-statistical techniques to optimize analysis of data
        • 5. Supporting sampling and analysis
        • 6. Reviewing data
        • 7. Providing support for data discovery
        • 8. Acting as SMEs for questions related to database administration
  • 7. Works Cited / Recommended
    • 7.1. 17. When reviewing data access plans，sequential searching 顺序检索 is slowing the database. One way to fix this is. A：converting it to an in-memory database B：adding more memory C：reducing the number of database users D：creating new indexes. E：moving the database to the cloud 正确答案：D 你的答案：D 解析：D来源：5.2.2题解：6）建立索引。索引是用于访问数据库数据的过程中优化查询（数据检索）性能的另一个选择。在许多情况下，索引可以提高查询性能。数据库管理员或数据库开发人员必须为数据库表选择和定义适当的索引。主要的RDBMS产品支持多种类型的索引。索引可以是唯一的或非唯一的、集群的或非集群的、分区的或非分区的、单列或多列、b树、位图或散列等多种类型。如果没有适当的索引，DBMS将读取表中的每一行（表扫描）以检索所有数据。
    • 7.2. 21. Non value-added information is often not removed because A：We might need the information at a later stage B：Legislation is unclear on what should be kept C：The policies are unclear of what is defined as non-value-added，storage is cheap so there is no cost driver, and it takes more effort to dispose than to keep. D：Data is an asset. It is likely to be recognized as valuable in the future E：It should not be removed all data is value-added 正确答案：C 你的答案：D 解析：这些政策不清楚什么是非增值的，存储很便宜，所以没有成本驱动因素，而且处置（处置）比保留需要更多的努力。
    • 7.3. 22. To which of the following initiatives was the establishment of an industry Meta-Data standard 行业标准 essential 主要的? A：BASEL lI/Sox B：EDI C：JSON D：Internet Protocols E：Proprietary XML 正确答案：B 你的答案：B 解析：6.2.2. EDI（Electronic Data Interchange）电子数据交换，是利用电随应用系统，由一台电菌运用标准协定及统一标准数螺格式，经过电子化的载螺传递方式，将数据传送到另一台电脑的电脑应用系统，EDI的标准协定使电脑间的载螺传输能够自动了解、处理和回应。