data warehouse architecture
The Rensselaer data warehouse architecture has primarily adhered to Ralph Kimball's methodologies (as described in The Data Warehouse Lifecycle Toolkit, a book by Kimball et al, also available online within Rensselaer).
architecture as a blueprint to success
Ralph Kimball compares the need for a data warehouse architecture with the need for a blueprint when constructing a house. Similar to such a blueprint, developing a data warehouse architecture yields:
- a communications tool, providing members of the Data Warehouse Group and beyond a clear picture of what makes up the data warehouse, how such components work together, etc.
- a learning tool, helping to avoid the common trial-and-error approach to learning about a new system.
- a cross-check for the project plan, ensuring that the project plan is accurate, reasonable (in terms of timeframes and resources), and comprehensive (i.e. that key architectural tasks are not forgotten).
goals of a sound architecture
A sound data warehouse architecture enables:
- extensibility by anticipating future end-user needs and providing a "roadmap" that reveals where such needs are addressed (e.g. where and how does the financial budget management tool fit into the data warehouse architecture?).
- reusability by documenting reusable components, processes, etc. (e.g. after documenting and revising the process of building the first data mart, the process should be reused to build subsequent data marts).
- improved productivity by enabling reusability and revealing where specific tools may be necessary to automate data warehouse processes (e.g. how will the incoming data be analyzed and cleansed?).
data warehousing tools
informatica ETL software