Principles of data fabric : become a data-driven organization by implementing data fabric solutions efficiently /

Data can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mezzetta, Sonia (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:1st edition.
Temas:
Data integration (Computer science)
Big data.
Database management.
Intégration de données (Informatique)
Données volumineuses.
Bases de données > Gestion.
Big data
Database management
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: The Building Blocks
  • Chapter 1: Introducing Data Fabric
  • What is Data Fabric?
  • What Data Fabric is
  • What Data Fabric is not
  • Why is Data Fabric important?
  • Drawbacks of centralized data management
  • Decentralized data management
  • Building Data Fabric architecture
  • Data Fabric building blocks
  • Data Fabric principles
  • The four Vs
  • Data Governance
  • Knowledge layer
  • Data Integration
  • Self-Service
  • Operational Data Governance models
  • Summary
  • Chapter 2: Show Me the Business Value
  • Digital transformation
  • Data monetization
  • Revenue
  • Cost savings
  • Data Fabric's value proposition
  • Trusting your decisions with governed data
  • Creating a unified view of your data with intelligent Data Integration
  • Gaining a competitive advantage with Self-Service
  • Data Fabric for large, medium, and small enterprises
  • Large enterprise organizations
  • Small and medium-sized businesses
  • Summary
  • Part 2: Complementary Data Management Approaches and Strategies
  • Chapter 3: Choosing between Data Fabric and Data Mesh
  • Introducing Data Mesh
  • Domain ownership
  • Data as a product
  • Self-Serve data platform
  • Federated computational governance
  • Comparing Data Fabric and Data Mesh
  • Objectives
  • Data Fabric and Data Mesh's friendship
  • How Data Fabric supports a federated-based organization
  • How Data Fabric manages data as a product
  • Self-Service data platform via a Data Fabric and Data Mesh architecture
  • Federated computational governance with Data Fabric
  • Summary
  • Chapter 4: Introducing DataOps
  • What is DataOps?
  • DataOps' principles
  • The evolution of DataOps
  • DataOps' dimensions
  • MLOps and AIOps depend on DataOps
  • DataOps' value
  • From traditional Data Quality to data observability
  • Data Fabric with DataOps
  • Develop
  • Orchestrate
  • Test
  • Deploy
  • Monitor
  • Summary
  • Chapter 5: Building a Data Strategy
  • Why create a data strategy?
  • A data maturity framework
  • A data maturity assessment
  • Creating a data strategy
  • Topics in a data strategy document
  • Creating a data strategy document
  • Data strategy implementation
  • Summary
  • Part 3: Designing and Realizing Data Fabric Architecture
  • Chapter 6: Designing a Data Fabric Architecture
  • Introduction to enterprise architecture
  • Types of enterprise architecture
  • Data Fabric principles
  • Data Fabric architecture principles
  • Data Fabric architecture layers
  • Data Governance
  • Data Integration
  • Self-Service
  • Summary
  • Chapter 7: Designing Data Governance
  • Data Governance architecture
  • Metadata-driven architecture
  • EDA
  • Metadata as a service
  • Metadata collection
  • Metadata integration
  • Metadata-based events
  • The Data Governance layer
  • Active metadata
  • Life cycle governance
  • Operational models
  • The Data Fabric's governance applied

Ejemplares similares