Data WareHouse

Data WareHouse

1. Pool of data produced to support decision making

   • Also a repository of current and historial data of potential interest to managers throughout the organization

2. In the 2000's

   • Consolidation of DW/BI industry
   • Data warehouse appliances emerged
   • Business intelligence popularized
   • Data mining and predictive modeling

3. Fundamental characteristics

   • Subject oriented
     • Data are organized by detailed subject, such as sales, products, or customers, containing only information relevant for decision support.
   • Integrated
     • Data warehouses must place data from different sources into a consistent format.
   • Time variant
     • The data do not necessarily provide current status, they detect trends, deviations and long-term relationships for forecasting and comparaciones.
   • Nonvolatile
     • Users cannot change or update the data
   • Web based
     • Data warehouse are typically designed to provide an efficient computing environment for web-bases applications
   • Relational/multidimensional
     • A data warehouse uses either a relational structure or a multidimensional structure.
   • Client /server
     • A data warehouse uses the client/server architecture to provide easy access for end users.
   • Real time
     • Newer data warehouses provide real-time, or active, data-access and analysis capabilities
   • Include metadata
     • A data warehouse contains metadata about how the data are organized and how effectively use them

4. whereas a data warehouse is a repository of data, data warehousing is literally the entire process

5. 3 types of warehouses

   • Data Marts (ODS)
     • Is usually smaller and focuses on a particular subject or department.
       • Is a subset that is created directly from the data warehouse
   • Operational Data Stores (ODS)
     • Provides a fairly recent form of customer information file
       • Is often used as an interim staging area for a data warehouse
   • Enterprise Data Warehouse (EDW)
     • Is a large scale of data warehouse that is used across the enterprise for decision support
       • EDW are used to provide data for many types of DSS, including CRM, supply chain management, business performance, business monitoring, etc.

6. Major components of the data warehousing process

   • Data sources
   • Data extraction
   • Data loading
   • Comprehensive data base
   • Metadata
   • Middleware tools

7. Purpose of a data warehouse is to integrate data from multiple systems

   • Enterprise application integration (EAI)
   • Enterprise information integration (EII)

8. Heart of technical side of data warehousing process is

   • Extraction
   • Transformation
   • Load

9. The Inmon Model EDW Approach

10. Kimball model

11. Analysis of DW

    • OLAP
      • OLTP