Data Warehousing: Staging Area (Definitions)

"The staging area is where data from the operational systems is first brought together. It is an informally designed and maintained grouping of data that may or may not have persistence beyond the load process." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)

"A database design used to preprocess data before loading it into a different structure." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A place where data in transit is placed, usually coming from the legacy environment prior to entering the ETL layer of processing." (William H Inmon, "Building the Data Warehouse", 2005)

"Place where data is stored while it is being prepared for use, typically where data used by ETL processes is stored. This may encompass everything from where the data is extracted from its original source until it is loaded into presentation servers for end user access. It may also be where data is stored to prepare it for loading into a normalized data warehouse." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)

[initial staging area:] "The area where the copy of the data from sources persists as a result of the extract/data movement process. (Data from real-time sources that is intended for real-time targets only is not passed through extract/data movement and does not land in the initial staging area.) The major purpose for the initial staging area is to persist source data in nonvolatile storage to achieve the “pull it once from source” goal." (Anthony D Giordano, "Data Integration Blueprint and Modeling", 2010)

"A location where data that is to be transformed is held in abeyance waiting for other events to occur" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"An area for the provision of data, where data is stored temporarily for validation and correction before it will be imported into the target database. A staging area thereby provides technical support for the first time right principle of data management." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)