Deciphering Data Architectures James Serra

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Spis treści książki

• Foreword
• Preface
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Foundation
• 1. Big Data
  • What Is Big Data, and How Can It Help You?
  • Data Maturity
    • Stage 1: Reactive
    • Stage 2: Informative
    • Stage 3: Predictive
    • Stage 4: Transformative
  • Self-Service Business Intelligence
  • Summary
• 2. Types of Data Architectures
  • Evolution of Data Architectures
  • Relational Data Warehouse
  • Data Lake
  • Modern Data Warehouse
  • Data Fabric
  • Data Lakehouse
  • Data Mesh
  • Summary
• 3. The Architecture Design Session
  • What Is an ADS?
  • Why Hold an ADS?
  • Before the ADS
    • Preparing
    • Inviting Participants
  • Conducting the ADS
    • Introductions
    • Discovery
    • Whiteboarding
  • After the ADS
  • Tips for Conducting an ADS
  • Summary
• II. Common Data Architecture Concepts
• 4. The Relational Data Warehouse
  • What Is a Relational Data Warehouse?
  • What a Data Warehouse Is Not
  • The Top-Down Approach
  • Why Use a Relational Data Warehouse?
  • Drawbacks to Using a Relational Data Warehouse
  • Populating a Data Warehouse
    • How Often to Extract the Data
    • Extraction Methods
    • How to Determine What Data Has Changed Since the Last Extraction
  • The Death of the Relational Data Warehouse Has Been Greatly Exaggerated
  • Summary
• 5. Data Lake
  • What Is a Data Lake?
  • Why Use a Data Lake?
  • Bottom-Up Approach
  • Best Practices for Data Lake Design
  • Multiple Data Lakes
    • Advantages
      • Organizational structure and ownership
      • Compliance, governance, and security
      • Cloud subscription, service limits, and policies
      • Performance, availability, and disaster recovery
      • Data retention and environment management
    • Disadvantages
  • Summary
• 6. Data Storage Solutions and Processes
  • Data Storage Solutions
    • Data Marts
    • Operational Data Stores
      • Use case
    • Data Hubs
  • Data Processes
    • Master Data Management
      • Use case
    • Data Virtualization and Data Federation
      • Virtualization as a replacement for the data warehouse
      • Virtualization as a replacement for ETL or data movement
    • Data Catalogs
    • Data Marketplaces
  • Summary
• 7. Approaches to Design
  • Online Transaction Processing Versus Online Analytical Processing
  • Operational and Analytical Data
  • Symmetric Multiprocessing and Massively Parallel Processing
  • Lambda Architecture
  • Kappa Architecture
  • Polyglot Persistence and Polyglot Data Stores
  • Summary
• 8. Approaches to Data Modeling
  • Relational Modeling
    • Keys
    • EntityRelationship Diagrams
    • Normalization Rules and Forms
    • Tracking Changes
  • Dimensional Modeling
    • Facts, Dimensions, and Keys
    • Tracking Changes
    • Denormalization
  • Common Data Model
  • Data Vault
  • The Kimball and Inmon Data Warehousing Methodologies
    • Inmons Top-Down Methodology
    • Kimballs Bottom-Up Methodology
    • Choosing a Methodology
    • Hybrid Models
  • Methodology Myths
  • Summary
• 9. Approaches to Data Ingestion
  • ETL Versus ELT
  • Reverse ETL
  • Batch Processing Versus Real-Time Processing
    • Batch Processing Pros and Cons
    • Real-Time Processing Pros and Cons
  • Data Governance
  • Summary
• III. Data Architectures
• 10. The Modern Data Warehouse
  • The MDW Architecture
  • Pros and Cons of the MDW Architecture
  • Combining the RDW and Data Lake
    • Data Lake
    • Relational Data Warehouse
  • Stepping Stones to the MDW
    • EDW Augmentation
      • How it works
      • Benefits
      • Challenges
      • Migration
    • Temporary Data Lake Plus EDW
      • How it works
      • Benefits
      • Challenges
      • Migration
    • All-in-One
      • How it works
      • Benefits
      • Challenges
      • Migration
  • Case Study: Wilson & Gunkerks Strategic Shift to an MDW
    • Challenge
    • Solution
    • Outcome
  • Summary
• 11. Data Fabric
  • The Data Fabric Architecture
    • Data Access Policies
    • Metadata Catalog
    • Master Data Management
    • Data Virtualization
    • Real-Time Processing
    • APIs
    • Services
    • Products
  • Why Transition from an MDW to a Data Fabric Architecture?
  • Potential Drawbacks
  • Summary
• 12. Data Lakehouse
  • Delta Lake Features
  • Performance Improvements
  • The Data Lakehouse Architecture
  • What If You Skip the Relational Data Warehouse?
  • Relational Serving Layer
  • Summary
• 13. Data Mesh Foundation
  • A Decentralized Data Architecture
  • Data Mesh Hype
  • Dehghanis Four Principles of Data Mesh
    • Principle #1: Domain Ownership
    • Principle #2: Data as a Product
    • Principle #3: Self-Serve Data Infrastructure as a Platform
    • Principle #4: Federated Computational Governance
  • The Pure Data Mesh
  • Data Domains
  • Data Mesh Logical Architecture
  • Different Topologies
  • Data Mesh Versus Data Fabric
  • Use Cases
  • Summary
• 14. Should You Adopt Data Mesh? Myths, Concerns, and the Future
  • Myths
    • Myth: Using Data Mesh Is a Silver Bullet That Solves All Data Challenges Quickly
    • Myth: A Data Mesh Will Replace Your Data Lake and Data Warehouse
    • Myth: Data Warehouse Projects Are All Failing, and a Data Mesh Will Solve That Problem
    • Myth: Building a Data Mesh Means Decentralizing Absolutely Everything
    • Myth: You Can Use Data Virtualization to Create a Data Mesh
  • Concerns
    • Philosophical and Conceptual Matters
    • Combining Data in a Decentralized Environment
    • Other Issues of Decentralization
    • Complexity
    • Duplication
    • Feasibility
    • People
    • Domain-Level Barriers
  • Organizational Assessment: Should You Adopt a Data Mesh?
  • Recommendations for Implementing a Successful Data Mesh
  • The Future of Data Mesh
  • Zooming Out: Understanding Data Architectures and Their Applications
  • Summary
• IV. People, Processes, and Technology
• 15. People and Processes
  • Team Organization: Roles and Responsibilities
    • Roles for MDW, Data Fabric, or Data Lakehouse
    • Roles for Data Mesh
      • Domain teams
      • Self-service data infrastructure platform team
      • Federated computational governance platform team
  • Why Projects Fail: Pitfalls and Prevention
    • Pitfall: Allowing Executives to Think That BI Is Easy
    • Pitfall: Using the Wrong Technologies
    • Pitfall: Gathering Too Many Business Requirements
    • Pitfall: Gathering Too Few Business Requirements
    • Pitfall: Presenting Reports Without Validating Their Contents First
    • Pitfall: Hiring an Inexperienced Consulting Company
    • Pitfall: Hiring a Consulting Company That Outsources Development to Offshore Workers
    • Pitfall: Passing Project Ownership Off to Consultants
    • Pitfall: Neglecting the Need to Transfer Knowledge Back into the Organization
    • Pitfall: Slashing the Budget Midway Through the Project
    • Pitfall: Starting with an End Date and Working Backward
    • Pitfall: Structuring the Data Warehouse to Reflect the Source Data Rather Than the Businesss Needs
    • Pitfall: Presenting End Users with a Solution with Slow Response Times or Other Performance Issues
    • Pitfall: Overdesigning (or Underdesigning) Your Data Architecture
    • Pitfall: Poor Communication Between IT and the Business Domains
  • Tips for Success
    • Dont Skimp on Your Investment
    • Involve Users, Show Them Results, and Get Them Excited
    • Add Value to New Reports and Dashboards
    • Ask End Users to Build a Prototype
    • Find a Project Champion/Sponsor
    • Make a Project Plan That Aims for 80% Efficiency
  • Summary
• 16. Technologies
  • Choosing a Platform
    • Open Source Solutions
    • On-Premises Solutions
    • Cloud Provider Solutions
  • Cloud Service Models
    • Major Cloud Providers
    • Multi-Cloud Solutions
  • Software Frameworks
    • Hadoop
    • Databricks
    • Snowflake
  • Summary
• Index

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