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  2. Relational model - Wikipedia

    en.wikipedia.org/wiki/Relational_model

    A table may contain both duplicate rows and duplicate columns, and a table's columns are explicitly ordered. SQL uses a Null value to indicate missing data, which has no analog in the relational model. Because a row can represent unknown information, SQL does not adhere to the relational model's Information Principle. [7]: 153–155, 162

  3. SQL syntax - Wikipedia

    en.wikipedia.org/wiki/SQL_syntax

    SQL statements also include the semicolon (";") statement terminator. Though not required on every platform, it is defined as a standard part of the SQL grammar. Insignificant whitespace is generally ignored in SQL statements and queries, making it easier to format SQL code for readability.

  4. Database normalization - Wikipedia

    en.wikipedia.org/wiki/Database_normalization

    This table is in 4NF, but the Supplier ID is equal to the join of its projections: {{Supplier ID, Title}, {Title, Franchisee ID}, {Franchisee ID, Supplier ID}}. No component of that join dependency is a superkey (the sole superkey being the entire heading), so the table does not satisfy the ETNF and can be further decomposed: [ 12 ]

  5. Relational database - Wikipedia

    en.wikipedia.org/wiki/Relational_database

    A Relational Database Management System (RDBMS) is a type of database management system that stores data in a structured format using rows and columns. Many relational database systems are equipped with the option of using SQL (Structured Query Language) for querying and updating the database. [3]

  6. Select (SQL) - Wikipedia

    en.wikipedia.org/wiki/Select_(SQL)

    With the same table, the query SELECT C1 FROM T will result in the elements from the column C1 of all the rows of the table being shown. This is similar to a projection in relational algebra, except that in the general case, the result may contain duplicate rows. This is also known as a Vertical Partition in some database terms, restricting ...

  7. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]

  8. Database index - Wikipedia

    en.wikipedia.org/wiki/Database_index

    The non-clustered index tree contains the index keys in sorted order, with the leaf level of the index containing the pointer to the record (page and the row number in the data page in page-organized engines; row offset in file-organized engines). In a non-clustered index, The physical order of the rows is not the same as the index order.

  9. Change data capture - Wikipedia

    en.wikipedia.org/wiki/Change_data_capture

    This technique can either supplement or complement timestamps and versioning. It can configure an alternative if, for example, a status column is set up on a table row indicating that the row has changed (e.g., a boolean column that, when set to true, indicates that the row has changed).