Data management at scale : best practices for enterprise architecture /

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As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you'll learn...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Strengholt, Piethein (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, [2020]
Edición:First edition.
Temas:
Database management.
Electronic data processing > Management.
Information technology > Management.
Database management
Electronic data processing > Management
Information technology > Management
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Copyright
  • Table of Contents
  • Foreword
  • Preface
  • Navigating Through This Book
  • Conventions Used in This Book
  • O'Reilly Online Learning
  • How to Contact Us
  • Acknowledgments
  • Chapter 1. The Disruption of Data Management
  • Data Management
  • Analytics Is Fragmenting the Data Landscape
  • Speed of Software Delivery Is Changing
  • Networks Are Getting Faster
  • Privacy and Security Concerns Are a Top Priority
  • Operational and Transactional Systems Need to Be Integrated
  • Data Monetization Requires an Ecosystem-to-Ecosystem Architecture
  • Enterprises Are Saddled with Outdated Data Architectures
  • Enterprise Data Warehouse and Business Intelligence
  • Data Lake
  • Centralized View
  • Summary
  • Scaled Architecture
  • Chapter 2. Introducing the Scaled Architecture: Organizing Data at Scale
  • Universally Acknowledged Starting Points
  • Each Application Has an Application Database
  • Applications Are Specific and Have Unique Context
  • Golden Source
  • There's No Escape from the Data Integration Dilemma
  • Applications Play the Roles of Data Providers and Data Consumers
  • Key Theoretical Considerations
  • Object-Oriented Programming Principles
  • Domain-Driven Design
  • Business Architecture
  • Communication and Integration Patterns
  • Point-to-Point
  • Silos
  • Hub-Spoke Model
  • Scaled Architecture
  • Golden Sources and Domain Data Stores
  • Data Delivery Contracts and Data Sharing Agreements
  • Eliminating the Siloed Approach
  • Domain-Driven Design on an Enterprise Scale
  • Read-Optimized Data
  • Data Layer as a Holistic Picture
  • Metadata and the Target Operating Model
  • Summary
  • Chapter 3. Managing Vast Amounts of Data: The Read-Only Data Stores Architecture
  • Introducing the RDS Architecture
  • Command and Query Responsibility Segregation
  • What Is CQRS?
  • CQRS at Scale
  • Read-Only Data Store Components and Services
  • Metadata
  • Data Quality
  • RDS Tiers
  • Data Ingestion
  • Integrating Commercial Off-the-Shelf Solutions
  • Extracting Data from External APIs and SaaSs
  • Historical Data Service
  • Design Variations
  • Data Replication
  • Access Layer
  • File Manipulation Service
  • Delivery Notification Service
  • De-Identification Service
  • Distributed Orchestration
  • Intelligent Consumption Services
  • Populating RDSs on Demand
  • RDS Direct Usage Considerations
  • Summary
  • Chapter 4. Services and API Management: The API Architecture
  • Introducing the API Architecture
  • What Is Service-Oriented Architecture?
  • Enterprise Application Integration
  • Service Orchestration
  • Service Choreography
  • Public Services and Private Services
  • Service Models and Canonical Data Models
  • Similarities Between SOA and Enterprise Data Warehousing Architecture
  • Modern View on SOA
  • API Gateway
  • Responsibility Model
  • The New Role of the ESB
  • Service Contracts
  • Service Discovery
  • Microservices

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