Top-Down vs Bottom-Up Data Warehouse Design
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Questions and Answers

What does data granularity in a data warehouse refer to?

  • The number of columns in a table
  • The level of detail (correct)
  • The amount of data stored
  • The speed of data retrieval
  • Which approach involves building bite-size departmental data marts?

  • Top-down approach
  • Bottom-up approach (correct)
  • Centralized data warehouse approach
  • Independent data marts approach
  • Should you look at the big picture of your organization and build a mammoth data warehouse?

  • Yes, always (correct)
  • No, never
  • Depends on the size of the organization
  • Only for small organizations
  • Which data warehouse design approach involves creating dependent data marts?

    <p>Data mart bus approach</p> Signup and view all the answers

    Should you start by building a pilot or go directly with a full-fledged implementation when developing a data warehouse?

    <p>Depends on the organization's budget</p> Signup and view all the answers

    What is a key advantage of the Top-Down Approach?

    <p>A truly corporate effort, an enterprise view of data</p> Signup and view all the answers

    Which characteristic is a disadvantage of the Top-Down Approach?

    <p>Takes longer to build even with an iterative method</p> Signup and view all the answers

    What is a key advantage of the Bottom-Up Approach?

    <p>Permeates redundant data in every data mart</p> Signup and view all the answers

    Which characteristic is a disadvantage of the Bottom-Up Approach?

    <p>Proliferates unmanageable interfaces</p> Signup and view all the answers

    Why is a top-down approach considered more centralized compared to a bottom-up approach?

    <p>Single, central storage of data about the content</p> Signup and view all the answers

    Which approach is more likely to lead to inconsistent and irreconcilable data?

    <p>Perpetuates redundant data in every data mart</p> Signup and view all the answers

    What is the relationship between data marts and the enterprise data warehouse?

    <p>Data marts depend on the enterprise data warehouse for data feed.</p> Signup and view all the answers

    In the data-mart bus approach, what is the principal notion behind conforming dimensions among data marts?

    <p>To integrate data marts into logically integrated supermarts.</p> Signup and view all the answers

    What is the main idea behind the top-down approach in building a data warehouse?

    <p>Beginning with a centralized data warehouse and then creating dependent data marts.</p> Signup and view all the answers

    What defines the bottom-up approach to building a data warehouse?

    <p>Creating data marts first and then integrating them into a centralized warehouse.</p> Signup and view all the answers

    Why is conforming dimensions among various data marts important in the context of the data-mart bus approach?

    <p>To have logically integrated supermarts providing an enterprise view of the data.</p> Signup and view all the answers

    How are 'data warehouses' and 'data marts' related in terms of different architectures?

    <p>'Data warehouses' and 'data marts' can have different integration methods in different architectures.</p> Signup and view all the answers

    Study Notes

    Data Warehouse Approaches and Architectures

    • There are two approaches to building a data warehouse: top-down and bottom-up.

    • Top-down approach:

      • Advantages: truly corporate effort, enterprise view of data, inherently architected, and single central storage of data.
      • Disadvantages: takes longer to build, high exposure to failure, high outlay without proof of concept, and needs high level of cross-functional skills.
    • Bottom-up approach:

      • Advantages: faster and easier implementation, favourable return on investment, less risk of failure, and allows project team to learn and grow.
      • Disadvantages: each data mart has its own narrow view of data, perpetuates inconsistent and irreconcilable data, and proliferates unmanageable interfaces.

    A Practical Approach

    • Ralph Kimball's practical approach consists of four steps:
      • Plan and define requirements at the overall corporate level.
      • Create a surrounding architecture for a complete warehouse.
      • Conform and standardize the data content.

    Data Granularity

    • In an operational system, data is usually kept at the lowest level of detail.
    • In a data warehouse, data is summarized at different levels, and the level of detail is referred to as data granularity.
    • The lower the level of detail, the finer is the data granularity.

    Data Warehouses and Data Marts

    • Before building a data warehouse, consider the following questions:
      • Top-down or bottom-up approach?
      • Enterprise-wide or departmental?
      • Which first—data warehouse or data mart?
      • Build pilot or go with a full-fledged implementation?
      • Dependent or independent data marts?

    Data-Mart Bus

    • The data marts depend on the enterprise data warehouse for data feed.
    • The centralized data warehouse forms the hub to feed data to the data marts on the spokes.
    • Information delivery can be both from the centralized data warehouse and the dependent data marts.

    Data Warehousing Architecture

    • The five major types of data warehouse architectures differ in how data is integrated and stored, and how "data warehouses" and "data marts" are related.

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    Description

    Learn about the advantages and disadvantages of top-down and bottom-up approaches in data warehouse design. Explore the differences in corporate effort, architecture, centralized storage, skills required, exposure to risk, and speed of implementation.

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