Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs


Published: 10 April 2018 ID: G00342254

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Summary

The evolving nature of data in 2018 is forcing data management solutions for analytics (DMSA) architectures to evolve too. Technical professionals should adopt the logical data warehouse as the modern, "next-gen" data warehouse that uses a multiengine approach to fulfill conflicting demands.

Table of Contents

  • Analysis
    • The Complementary DMSA Components of the LDW
    • LDW Service Levels: Compromise, Contender, Candidate
    • The Importance of Shared Data Access
    • The LDW: Repository Style or Component
      • Data Stores
    • The LDW: Data Virtualization Style or Component
      • Virtualization Data Sources
      • Data Virtualization Components
    • The LDW: Distributed Process Style or Component
      • DP Data Sources
      • Data Ingest and Integration
      • DP Style: The Data Lake
    • Common Components
      • Data Integration
      • Access Management and Delivery
      • Information Governance
    • Architectural Considerations
      • Placement of Data and Requirements
      • Deployment
      • The Role of Mart, Warehouse, Sandbox and Lake
      • Be Ready for LDW Workloads to Shift Over Time
    • Strengths
    • Weaknesses
  • Guidance
  • The Details
    • Case Study: Logitech
    • The Advancing Stages of LDW Adoption
      • Initiating: Build a Sound Data Platform
      • Intermediate: Evolve to the LDW
      • Advanced: Optimize the LDW to Support All Requirements
    • An Example of the LDW Applied to Healthcare
  • Gartner Recommended Reading
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