Gartner Research

The Structured Components of the Logical Data Warehouse: Enterprise Warehouse, Mart, Hub and ODS

Published: 16 October 2019

Summary

The traditional data warehouse is alive and well. It is used stand-alone or as an essential component of the LDW. The data warehouse mission remains the same, but its implementation has changed. Data and analytics technical professionals responsible for data management should continue to use DWs.

Included in Full Research

  • Essential Components for Handling Structured Data
  • Summary of the Data Warehousing Mission vs. Implementation
  • Extensions to the Structured Data Stores and Servers
  • Relational Massively Parallel Processing (MPP)
  • Column Stores and In-Memory Column Stores
  • Data Models
    • Higher Normal Forms — Third Normal Form (3NF) and Beyond
    • Dimensional Modelling
    • Data Vault Modelling
    • Schema on Read
    • Transformation on the Fly and Use of Views
  • Handling Less Structured Data
    • Textual Data
    • Optimization of Data Processing and Data Formatting
  • Autonomous Data Warehousing
  • Machine Learning Within and Alongside the Data Warehouse
  • Hardware Acceleration
  • Data Warehouse Automation
  • Automated Data Mapping
  • Automated Data Profiling
  • Automate Data Warehouse Testing
  • Data Marts
  • The Operational Data Store (ODS)
  • Augmented Transactions — aka HTAP
  • Data Hubs
  • Cloud Data Warehouse
  • Any Data, Anywhere, Any Language
  • Workloads Move — Bring Engine to Data or Data to Engine
  • Strengths
  • Weaknesses
  • Data Warehouse Mission vs. Implementation

Analysts:

Henry Cook

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