Data Warehousing

Data Warehousing

1. Definition

   • Pool of data produced to support decision making.
   • A repository of current and historical data of potential interest

2. BI

   • In conjunction with decision support system (DSS) is an important way to conduct business processes.

3. Characteristics

   • Subject oriented.
     • Data are organized by detailed subject, such as sales, products, etc.
   • Integrated
     • they must deal with naming conflicts and discrepancies among units of measure.
   • Time variant (time series)
     • A warehouse maintains historical data.
   • Nouvolatile.
     • users cannot change or update the data.

4. TYPES

   • Data Marts
     • smaller and focuses on a particular department.
       • a sub- set of a data warehouse, typically consisting of a single subject area
   • Operational Data Stores (ODS}
     • provides a fairly recent form of customer information file.
   • Enterprise Data Warehouses (EDWs)
     • Is a large-scale data warehouse that is used across the enterprise for decision support.

5. PROCESS OVERVIEW

   • Data sources
   • Data extraction and transformstion.
   • Data loading.
   • Compreheusive database.
   • Middleware tools.

6. Metadata

   • describe the structure of and some meaning about data, thereby contributing to their effective or ineffective use.

7. Arquitechture

   • Types
     • Client / Server
     • N-tier
       • The data warehouse itself.
       • Data acquisition (back-end) softvvare.
       • Client (front-end) software
   • Alternatives of DW Architecures
     • Independent data marts.
     • Data marl bus architecture.
     • Hub-spoke architecture
     • Centralized data warehouse.
     • Federated data warehouse.

8. ETL Processes

   • Extraction
     • Reading data from one or more data- bases
   • Transformation
     • converting the data from its previous form into the form in which it needs to be so that it can be placed into a data warehouse or simply an- other database
   • Load
     • putting the data into the data warehouse

9. Data Integration

   • Enterprise application integration (EAI) • Service-oriented architecture (SOA) • Enterprise information integration (Ell) • Extraction, transformation, and load (ETL)

10. Approaches

    • THE INMON MODEL: THE EDW APPROACH
      • The EDW approach does not preclude the creation of data marts. it provides a consistent and comprehensive view of the enterprise.
    • THE KIMBALL MODEL: THE DATA MART APPROACH
      • Focuses on the requests of a specific department such as marketing or sales. This model applies dimen- sional data modeling, which starts with tables

11. Daniel Garcia Mena A00813719

12. Representation

    • Star schema
      • Contains a central fact table surrounded by and connected to several dimension tables
    • Snowflake schema
      • Dimensions are normalized into multiple related tables whereas the star schema's dimensions are denormalized with each dimension being rep- resented by a single table.