School of Information Systems

Arsitektur Datawarehouse

The data warehouse is a collection of integrated, subject-oriented databases design to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time.

Characteristics of Data Warehousing

  • Subject oriented, data are organized by organized. Subject orientation provides a more comprehensive view of the organization
  • Integrated, DW must place data from different source into consistent format.
  • Time variant, a warehouse maintains historical data. The data do not necessarily provide current status.
  • Nonvolatile, after data entered into a data warehouse, user can’t change or update data.

Data Mart is a departmental data warehouse that stores only relevant data.

  • Dependent data mart

A subset that is created directly from a data warehouse

  • Independent data mart

A small data warehouse designed for a strategic business unit or a department

Data Warehousing Architectures

  • Three-tier architecture
    1. Data acquisition software (back-end)
    2. The data warehouse that contains the data & software
    3. Client (front-end) software that allows users to access and analyze data from the warehouse

DW Architecture

  • Two-tier architecture

First 2 tiers in three-tier architecture is combined into one.

DW Architecture 2

Ten factors that potentially affect the architecture selection decision:

  1. Information interdependence between organizational units
  2. Upper management’s information needs
  3. Urgency of need for a data warehouse
  4. Nature of end-user tasks
  5. Constraints on resources
  6. Strategic view of the data warehouse prior to implementation
  7. Compatibility with existing systems
  8. Perceived ability of the in-house IT staff
  9. Technical issues
  10. Social/political factors
  • Data integration Integration that comprises three major processes: data access, data federation, and change capture.
  • Enterprise application integration (EAI) A technology thatprovides a vehicle for pushing data from source systems into a data warehouse
  • Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources
  • Service-oriented architecture (SOA) A new way of integrating information systems

DW Architecture3

  • Direct benefits of a data warehouse
    • Allows end users to perform extensive analysis
    • Allows a consolidated view of corporate data
    • Better and more timely information
    • Enhanced system performance
    • Simplification of data access
  • Indirect benefits of data warehouse
    • Enhance business knowledge
    • Present competitive advantage
    • Enhance customer service and satisfaction
    • Facilitate decision making
    • Help in reforming business processes
  • Types of Data Warehouse

Information processing, analytical processing, and data mining are the three types of data warehouse applications that are discussed below:

  • Information Processing – A data warehouse allows to process the data stored in it. The data can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.
  • Analytical Processing – A data warehouse supports analytical processing of the information stored in it. The data can be analyzed by means of basic OLAP operations, including slice-and-dice, drill down, drill up, and pivoting.
  • Data Mining – Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using visualization tools.
Mediana Aryuni