DBMS: Flat File (Definitions)

"A simple data structure, often implemented on a mainframe, that relies on nonrelational files, such as IBM VSAM files." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit 2nd Ed ", 2002)

"A collection of records containing no data aggregates, nested repeated data items, or groups of data items." (William H Inmon, "Building the Data Warehouse", 2005)

"A collection of records that are related to one another that haven’t been organized to meet relational normal forms. Originally a file was stored only outside a database. Now you can refer to a table structured this way as a flat file." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A data structure that contains records with no inherent relationships." (Evan Levy & Jill Dyché, "Customer Data Integration", 2006)

"A term generally applying to an unstructured file, such as a text file." (Gavin Powell, "Beginning Database Design", 2006)

"A file in which the fields of records are simple atomic values." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"A plain old text file used to store data. A flat file isn't very fancy and provides few tools for querying, sorting, grouping, and performing other database operations but flat files are very easy to use." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"A file in which all the attribute fields are atomic, that is, single valued." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A file consisting of records of a single record type in which there is no embedded structure information that governs relationships between records." (Microsoft, "SQL Server 2012 Glossary", 2012)

"Plain text file wherein each line of the file holds one record, typically with fields separated by delimiters, such as commas or tabs." (Craig S Mullins, "Database Administration", 2012)

"A collection of records where the structure of each record is identical" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)