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5.10: Key Terms

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    Attribute: A record is one instance of a set of fields in a table. (5.5)

    Big Data: A broad term for data sets so large or complex that traditional data processing applications are inadequate (5.2)

    Database: An organized collection of related information. (5.3)

    Data Dictionary: Created to hold the metadata, defining the fields and structure of the database. To learn more, check out the example below and this resource from the University of Regina on data modeling and ERD diagrams. (5.6)

    Data Hierarchy: The structure and organization of data in a database (5.5)

    Data Integrity: Consistency among the stored data. (5.7)

    Database Management Systems (DBMS): Programs that provide user-friendly, interfaces to view and change a database, create queries, and develop reports. (5.3)

    Database Manipulation: The use of a programming language to modify data in a database to make it easier to view or for the purpose of decision making. (5.8)

    Data Model: of a database is the logical structure of the data items and their relationships. (5.4)

    Data Sharing: The integration of the whole data in an organization leads to the ability to produce more information from a given amount of data. When the data is centralized in one database it makes it easier.(5.7)

    Data Types: When defining the fields in a database table, we must give each field a data type. For example, the field ‘StudentName’ is text string, while ‘EnrollmentCapacity’ is number. Most modern databases allow for several different data types to be stored. (5.5)

    Entities: the data contained in the database (e.g., students, course, grade, classroom) (5.6)

    Entity Relationship Diagram (ERD): Which shows the relationships among the tables of data. When a database is being designed. (5.6)

    Flat File Database: Which contains only one file/table, with no relationships.A table created in a spreadsheet would be considered a flat file. (5.4)

    Hierarchical Database Model: Popular in the 1960s and 1970s, connected data together in a hierarchy, allowing for a parent/child relationship between data.(5.4)

    Metadata: Data that describes other data. (5.2)

    Normalization: To normalize a database means to design it in a way that reduces data redundancy; and ensure data integrity. (5.6)

    NoSQL Databases: Not Only SQL Database. A type of database that operates using means other than relational tables. NoSQL became popular with the growth of Web 2.0 and the need for faster data retrieval. (5.4)

    Query-by-Example (QBE): A graphical query tool, to retrieve data though visualized commands. (5.8)

    Record(row): Records as the rows of the table. (5.5)

    Redundant Data: Redundant data is data that is repeated in a database, which can cause the data set to be inconsistent. In the database approach, ideally each data item is stored in only one place in the database. (5.7)

    Relational Database: One in which data is organized into one or more related tables. (5.3)

    Relationships: between data items (e.g., students get grades in courses that are offered in classrooms) (5.6)

    Scale: Refers to a database getting larger and larger, being distributed on a larger number of computers connected via a network. (5.4)

    Schema: Developed to provide an overall description of the database.(5.6)

    Structured Query Language (SQL): The most common language for creating and manipulating databases. SQL inhabiting everything from desktop software, to high-powered enterprise products. (5.8)

    5.10: Key Terms is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

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