Data warehousing case study | TTV Westadata warehouses differ from operational databases in that they are subject oriented, integrated, time variant, non volatile, summarized, larger, not normalized, and perform olap. source, staging area, and target environments may have many different data structure formats as flat files, xml data sets, relational tables, non-relational sources, web log sources, legacy systems, and spreadsheets.
The Community Health Applied Research Network (CHARN) Dataextractionthe first step in any etl scenario is data extraction. the generic data warehouse architecture consists of three layers (data sources, dsa, and primary data warehouse) (inmon, 2002 and vassiliadis, 2000).
the data warehouse allows users to see where in the overall process a proposal or award is currently, including whether a proposal has been submitted to the sponsor or an award has been sent to accounting. introductiona data warehouse (dw) is a collection of technologies aimed at enabling the decision maker to make better and faster decisions.
each data source has its distinct set of characteristics that need to be managed in order to effectively extract data for the etl process. the process needs to effectively integrate systems that have different platforms, such as different database management systems, different operating systems, and different communications protocols.