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  2. Shadow table - Wikipedia

    en.wikipedia.org/wiki/Shadow_table

    For example, two tables, transaction_user and transaction_amount, would both contain the column "key", and keys between tables would match, making it easy to find both the user and the amount of a specific transaction if the key is known. This relational technology allowed people to correlate information stored in a primary table and its shadow.

  3. Data orientation - Wikipedia

    en.wikipedia.org/wiki/Data_orientation

    The two most common representations are column-oriented (columnar format) and row-oriented (row format). [ 1 ] [ 2 ] The choice of data orientation is a trade-off and an architectural decision in databases , query engines, and numerical simulations. [ 1 ]

  4. Query rewriting - Wikipedia

    en.wikipedia.org/wiki/Query_Rewriting

    Query rewriting is a typically automatic transformation that takes a set of database tables, views, and/or queries, usually indices, often gathered data and query statistics, and other metadata, and yields a set of different queries, which produce the same results but execute with better performance (for example, faster, or with lower memory use). [1]

  5. Schema matching - Wikipedia

    en.wikipedia.org/wiki/Schema_matching

    The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects.

  6. Schema crosswalk - Wikipedia

    en.wikipedia.org/wiki/Schema_crosswalk

    One scheme has one element that needs to be split up with different parts of it placed in multiple other elements in the second scheme ("one-to-many" mapping) One scheme allows an element to be repeated more than once while another only allows that element to appear once with multiple terms in it; Schemes have different data formats (e.g. John ...

  7. Wide-column store - Wikipedia

    en.wikipedia.org/wiki/Wide-column_store

    A wide-column store (or extensible record store) is a type of NoSQL database. [1] It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table. A wide-column store can be interpreted as a two-dimensional key–value store. [1]

  8. SQL syntax - Wikipedia

    en.wikipedia.org/wiki/SQL_syntax

    Each column in an SQL table declares the type(s) that column may contain. ANSI SQL includes the following data types. [14] Character strings and national character strings. CHARACTER(n) (or CHAR(n)): fixed-width n-character string, padded with spaces as needed; CHARACTER VARYING(n) (or VARCHAR(n)): variable-width string with a maximum size of n ...

  9. Data manipulation language - Wikipedia

    en.wikipedia.org/wiki/Data_manipulation_language

    In SQL, the data manipulation language comprises the SQL-data change statements, [3] which modify stored data but not the schema or database objects. Manipulation of persistent database objects, e.g., tables or stored procedures, via the SQL schema statements, [3] rather than the data stored within them, is considered to be part of a separate data definition language (DDL).