ISDS 2001 Test 2 Chapter 2

  1. What is a data warehouse?
    A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format

    "The data warehouse is a collection of integrated, subject-oriented databases designed to support DDS functions, where each unit of data is non-volatile and relevant to some moment in time"
  2. Why is a DW needed? What is the ultimate purpose of a DW in BI systems?
    To provide the single version of the truth
  3. The four major characteristics fo data warehousing:
    • Subject-oriented
    • Integrated
    • Time-variant
    • Nonvolatile
  4. Characteristics of Data Warehousing

    Data are organized by topics, such as sales, products, customers, etc. Best for providing a more comprehensive view of the organization; not only how a business is operating, but why.
  5. Characteristics of Data Warehousing

    Data from different sources are stored in a consisten format. Also clarity is obtained in unit of measures, naming/labeling of attributes, etc. (The assumption is the datat warehouse is totally integrated.)
  6. Characteristics of Data Warehousing

    Time Variant
    Provides data at various points in time (daily, weekly, monthly, quaterly, annually - historic and current data so as to analyze trends, deviations, compare and forcast outcomes, etc.) Every data warehouse should have a time variable.

    i.e. LSU enrollement, retention, graduation data
  7. There are three main types of Data Warehouses
    • Data Mart
    • Operational Data Stores (ODS)
    • Enterprise data warehouses (EDW)
  8. A Data Mart is:
    a subset of a data warehouse, usually consisting of a single subject area (marketing, sales, customer satisfaction inventory, production, etc.)
  9. A Data Mart has two componets which are
    • Dependent Data Mart
    • Independent Data Mart
  10. Dependent Data Mart (subset of Data Mart)
    Created directly from the data warehouse. This ensures that the user is using/viewing the same data available at all other users. EDW must be constructed first.
  11. Dependent Data Mart
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  12. Independant Data Mart (subset of Data Mart)
    A small warehouse designed for a department or strategic business unit (SBU). Its source is not an EDW
  13. Independent Data Mart
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  14. Operational Data Stores (ODS)
    A type of database often used as an interim (or staging) area for a data warehouse, especially for customer information files (CIF). Data are updated frequently through the course of business operations as opposed to the static contents of a data warehouse. (short-term memory)
  15. Operational Data Stores
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  16. Enterprise Data Warehouses (EDW)
    A large-scale data warehouse that is used across the enterprise/company for decision support. Being large-scaled, the EDW integrates data in standard format from many sources. (Direct TV and Enterprise use EDW)
  17. Enterprise Data Warehouse
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  18. Differentiate among a data mart, an ODS, and an EDW
    • An ODS (Operational Data Store) is the database from which a business operates on an ongoing basis.
    • Both an EDW and a Data Mart are data warehouses. An EDW (Enterprise Data Warehouse) is an all-encompanssing DW that covers all subject areas of interest to the entire organization. A data mart is a smaller DW designed around one problem, organizational function, topic, or other suitable focus area
  19. Data Mart
    is a subset of a data warehouse, typically consisting of a single subject area (smaller and focusing on a particular subject or department). A data mart can be either dependent or independent.
  20. Dependent Data Mart
    a subset that is created directly from the data warehouse

    it has the advantages of using a consistent data model and providing quality data
  21. Independent Data Mart
    a small warehouse designed for a strategic business unit or department, but its source is not an EDW
  22. Operational Data Stores (ODS)
    This type of database is often used as an interim staging area for a data warehouse.

    An ODS is similar to short-term memory in that it stores only very recent information
  23. Enterprise Data Warehouse (EDW)
    a large-scale data warehouse that is used across the enterprise for decision support.

    The large scale nature provides integration of data from many sources into a standard format for effective BI and decision support applications
  24. MetaData
    • Data about Data
    • descirbes the contents of a data warehouse, its structure (such as a field name, data type, default value, length), meaning, syntax and the manner of its use.
  25. MetaData - 3 Main Types
    Syntactic Metadata

    Structural Metadata

    Semantic Metadata
  26. MetaData Types

    Syntactic Metadata
    Data describing the syntax of data

    ex. "Dublin Core" may be expressed in plain text, HTML or XML
  27. MetaDate Types

    Structural Metadata
    Data describing the structure of the data

    ex. field name, data type, length, table relationships
  28. Meta Data Type

    Semantic Metadata
    Data describing the meaning of the data

    ex. a webpage may include metadata specifying what language it is written in
  29. Metadata in BI Architecture

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  30. Metadata in BI Architecture

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  31. DW Framework

    (Database can come from all: Database sources, ETL, Process, EDW, or Data mart)
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  32. Data Warehouse Architecture

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    • 1. Database Server (data acquisition software - backend)
    • 2. The data warehouse that contains the data & software (application server)
    • 3. Client (front-end) software that allows users to access and analyze data from the warehouse (Client Workstation)
  33. Data Warehouse Architecture

    Two-Tier Architecture
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    • 1. Application and database server
    • 2. Client Workstation
Card Set
ISDS 2001 Test 2 Chapter 2
Test 2 Chapter 2