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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
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
- Data Integration
- Access Management and Delivery
- Information Governance
- Placement of Data and Requirements
- The Role of Mart, Warehouse, Sandbox and Lake
- Be Ready for LDW Workloads to Shift Over Time
- The Complementary DMSA Components of the LDW
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
- Case Study: Logitech
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