T 1 DQ 2.3
docx
keyboard_arrow_up
School
Grand Canyon University *
*We aren’t endorsed by this school
Course
408
Subject
Information Systems
Date
Nov 24, 2024
Type
docx
Pages
1
Uploaded by MasterDugong739
A corporate information factory (CIF) gather data from operational sources and processes it into a repository in the integration layer of the database. The information is then sectioned into departmental data marts. This process is also know as extract, transformation, and load (ETL) because the data is extracted from sources, transformed into archives of columns and rows, and then loaded into a the Enterprise Data Warehouse. This is great for operational systems that have customer or vendor relationships. The downside is that it causes challenges connecting the analytic databases with the EDW's atomic repository.
Independent data mart or standalone data marts are created without a data warehouse. This
type of database architecture focuses on one department or subject. Departmental needs are
satisfied with this architecture and meets the analytical requirements for individual departments or subjects. This provides easy access to data that is frequently needed by the department. The downside is that when multiple departments are using standalone data marts numbers from the different departments will not match. The appeal is that there is no cross-organizational data to govern or coordinate and it funds IT projects since each data mart is created separately.
A dimensional data warehouse are used to categorize facts and measures. This data warehouse enables users to answer business questions about orders, invoices, and service calls. There are a lot of myths around dimensional data warehouse models: used only for summary data, they are departmental not enterprise, not scalable, only for predictable usage,
and they can't be integrated. This type of data warehouse can store as much history as a business determines is needed to include data beyond summary data, can be designed around business processes instead of departmentalized and allowing multiple functions, data
model can be scaled to any size to fit business needs, when designing the focus should be on measurement processes not predefined reports, and as long as the data warehouse conforms to the enterprise data warehouse bus architecture it can be integrated. The downside for this type of data model is the planning that needs to be done before setting up the design. The architecture has to be designed so that it does not fall into any of the myth categories as mentioned earlier.
Discover more documents: Sign up today!
Unlock a world of knowledge! Explore tailored content for a richer learning experience. Here's what you'll get:
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help