Data Management at Scale. 2nd Edition Piethein Strengholt

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• Foreword
• Preface
  • Why I Wrote This Book and Why Now
    • Who Is This Book For?
  • How to Read or Use This Book
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. The Journey to Becoming Data-Driven
  • Recent Technology Developments and Industry Trends
  • Data Management
  • Analytics Is Fragmenting the Data Landscape
  • The Speed of Software Delivery Is Changing
  • The Clouds Impact on Data Management Is Immeasurable
  • Privacy and Security Concerns Are a Top Priority
  • Operational and Analytical Systems Need to Be Integrated
  • Organizations Operate in Collaborative Ecosystems
  • Enterprises Are Saddled with Outdated Data Architectures
    • The Enterprise Data Warehouse: A Single Source of Truth
    • The Data Lake: A Centralized Repository for Structured and Unstructured Data
    • The Pain of Centralization
  • Defining a Data Strategy
  • Wrapping Up
• 2. Organizing Data Using Data Domains
  • Application Design Starting Points
    • Each Application Has a Data Store
    • Applications Are Always Unique
    • Golden Sources
    • The Data Integration Dilemma
    • Application Roles
  • Inspirations from Software Architecture
  • Data Domains
    • Domain-Driven Design
      • Bounded contexts
      • Ubiquitous language
    • Business Architecture
      • Business capabilities
      • Linking business capabilities with applications
      • Capability realizations
      • Shared capabilities
      • Complex applications
    • Domain Characteristics
      • Patterns for complex integration challenges
      • Strengths of business capability modeling
  • Principles for Distributed and Domain-Oriented Data Management
    • Design Principles for Data Domains
    • Best Practices for Data Providers
    • Domain Ownership Responsibilities
  • Transitioning Toward Distributed and Domain-Oriented Data Management
  • Wrapping Up
• 3. Mapping Domains to a Technology Architecture
  • Domain Topologies: Managing Problem Spaces
    • Fully Federated Domain Topology
      • The elephant in the room
    • Governed Domain Topology
    • Partially Federated Domain Topology
    • Value ChainAligned Domain Topology
    • Coarse-Grained Domain Topology
    • Coarse-Grained and Partially Governed Domain Topology
    • Centralized Domain Topology
    • Picking the Right Topology
  • Landing Zone Topologies: Managing Solution Spaces
    • Single Data Landing Zone
      • Organizing data products
      • Scaling a single landing zone
    • Source- and Consumer-Aligned Landing Zones
    • Hub Data Landing Zone
    • Multiple Data Landing Zones
    • Multiple Data Management Landing Zones
    • Practical Landing Zones Example
  • Wrapping Up
• 4. Data Product Management
  • What Are Data Products?
    • Problems with Combining Code, Data, Metadata, and Infrastructure
    • Data Products as Logical Entities
  • Data Product Design Patterns
    • What Is CQRS?
    • Read Replicas as Data Products
  • Design Principles for Data Products
    • Resource-Oriented Read-Optimized Design
    • Data Product Data Is Immutable
    • Using the Ubiquitous Language
    • Capture Directly from the Source
    • Clear Interoperability Standards
    • No Raw Data
    • Dont Conform to Consumers
    • Missing Values, Defaults, and Data Types
    • Semantic Consistency
    • Atomicity
    • Compatibility
    • Abstract Volatile Reference Data
    • New Data Means New Ownership
    • Data Security Patterns
    • Establish a Metamodel
    • Allow Self-Service
    • Cross-Domain Relationships
    • Enterprise Consistency
    • Historization, Redeliveries, and Overwrites
    • Business Capabilities with Multiple Owners
    • Operating Model
  • Data Product Architecture
    • High-Level Platform Design
    • Capabilities for Capturing and Onboarding Data
      • Ingestion method
      • Complex software packages
      • External APIs and SaaS providers
      • Lineage and metadata
    • Data Quality
    • Data Historization
      • Point-in-time
      • Interval
      • Append-only
      • Defining your historization strategy
  • Solution Design
    • Real-World Example
    • Alignment with Storage Accounts
    • Alignment with Data Pipelines
    • Capabilities for Serving Data
    • Data Serving Services
    • File Manipulation Service
    • De-Identification Service
    • Distributed Orchestration
    • Intelligent Consumption Services
    • Direct Usage Considerations
  • Getting Started
  • Wrapping Up
• 5. Services and API Management
  • Introducing API Management
  • What Is Service-Oriented Architecture?
    • Enterprise Application Integration
    • Service Orchestration
    • Service Choreography
    • Public Services and Private Services
    • Service Models and Canonical Data Models
    • Parallels with Enterprise Data Warehousing Architecture
      • Canonical model size
      • ESB as wrapper for legacy middleware
      • ESB managing application state
  • A Modern View of API Management
    • Federated Responsibility Model
    • API Gateway
    • API as a Product
    • Composite Services
    • API Contracts
    • API Discoverability
  • Microservices
    • Functions
    • Service Mesh
    • Microservice Domain Boundaries
  • Ecosystem Communication
  • Experience APIs
    • GraphQL
    • Backend for Frontend
  • Practical Example
  • Metadata Management
  • Read-Oriented APIs Serving Data Products
  • Wrapping Up
• 6. Event and Notification Management
  • Introduction to Events
    • Notifications Versus Carried State
    • The Asynchronous Communication Model
  • What Do Modern Event-Driven Architectures Look Like?
    • Message Queues
    • Event Brokers
    • Event Processing Styles
    • Event Producers
      • Application-generated events
      • Database-generated events
    • Event Consumers
    • Event Streaming Platforms
      • EDA reference architecture
      • Data product creation
      • Event stores
      • Streaming analytics
    • Governance Model
    • Event Stores as Data Product Stores
    • Event Stores as Application Backends
  • Streaming as the Operational Backbone
  • Guarantees and Consistency
    • Consistency Level
    • Processing Methods
    • Message Order
    • Dead Letter Queue
    • Streaming Interoperability
  • Governance and Self-Service
  • Wrapping Up
• 7. Connecting the Dots
  • Cross-Domain Interoperability
    • Quick Recap
    • Data Distribution Versus Application Integration
    • Data Distribution Patterns
    • Application Integration Patterns
    • Consistency and Discoverability
  • Inspiring, Motivating, and Guiding for Change
    • Setting Domain Boundaries
    • Exception Handling
  • Organizational Transformation
    • Team Topologies
    • Organizational Planning
  • Wrapping Up
• 8. Data Governance and Data Security
  • Data Governance
    • The Governance Framework
      • Roles
      • Creating the framework
      • Governance body
    • Processes: Data Governance Activities
    • Making Governance Effective and Pragmatic
    • Supporting Services for Data Governance
    • Data Contracts
      • Usage agreements
      • Best practices for getting started
  • Data Security
    • Current Siloed Approach
    • Trust Boundaries
    • Data Classifications and Labels
    • Data Usage Classifications
    • Unified Data Security
    • Identity Providers
    • Real-World Example
    • Typical Security Process Flow
    • Securing API-Based Architectures
    • Securing Event-Driven Architectures
  • Wrapping Up
• 9. Democratizing Data with Metadata
  • Metadata Management
  • The Enterprise Metadata Model
    • Practical Example of a Metamodel
    • Data Domains and Data Products
    • Data Models
      • Conceptual data models
      • Logical data models
      • Physical data models
      • Limitations and best practices
    • Data Lineage
    • Other Metadata Areas
  • The Metalake Architecture
    • Role of the Catalog
    • Role of the Knowledge Graph
      • Technologies and standards
      • Data fabric example
      • Data fabric for metadata management
      • Metalake solution design
  • Wrapping Up
• 10. Modern Master Data Management
  • Master Data Management Styles
  • Data Integration
  • Designing a Master Data Management Solution
  • Domain-Oriented Master Data Management
    • Reference Data
    • Master Data
      • Master identification numbers
      • MDM domains and data products
      • Domain-level MDM
    • MDM and Data Quality as a Service
  • MDM and Data Curation
    • Knowledge Exchange
    • Integrated Views
    • Reusable Components and Integration Logic
    • Republishing Data Through Integration Hubs
    • Republishing Data Through Aggregates
  • Data Governance Recommendations
  • Wrapping Up
• 11. Turning Data into Value
  • The Challenges of Turning Data into Value
  • Domain Data Stores
    • Granularity of Consumer-Aligned Use Cases
    • DDSs Versus Data Products
  • Best Practices
    • Business Requirements
    • Target Audience and Operating Model
    • Nonfunctional Requirements
    • Data Pipelines and Data Models
    • Scoping the Role Your DDSs Play
  • Business Intelligence
    • Semantic Layers
    • Self-Service Tools and Data
    • Best Practices
  • Advanced Analytics (MLOps)
    • Initiating a Project
    • Experimentation and Tracking
    • Data Engineering
    • Model Operationalization
    • Exceptions
  • Wrapping Up
• 12. Putting Theory into Practice
  • A Brief Reflection on Your Data Journey
  • Centralized or Decentralized?
  • Making It Real
    • Opportunistic Phase: Set Strategic Direction
    • Transformation Phase: Lay Out the Foundation
    • Optimization Phase: Professionalize Your Capabilities
  • Data-Driven Culture
    • DataOps
    • Governance and Literacy
  • The Role of Enterprise Architects
    • Blueprints and Diagrams
    • Modern Skills
    • Control and Governance
  • Last Words
• Index

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