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Before Insert; The four main types of triggers are: Row-level trigger: This gets executed before or after any column value of a row changes. Column-level trigger: This gets executed before or after the specified column changes. For each row type: This trigger gets executed once for each row of the result set affected by an insert/update/delete.
BEFORE INSERT triggers allow the modification of the values that shall be inserted into the table. AFTER INSERT triggers cannot modify the data anymore, but can be used to initiate actions on other tables, for example, to implement auditing mechanism.
In relational databases, the log trigger or history trigger is a mechanism for automatic recording of information about changes inserting or/and updating or/and deleting rows in a database table. It is a particular technique for change data capturing , and in data warehousing for dealing with slowly changing dimensions .
A current version is maintained for the table, or possibly a group of tables. This is stored in a supporting construct such as a reference table. When a change capture occurs, all data with the latest version number is considered to have changed. Once the change capture is complete, the reference table is updated with a new version number.
The term "transaction" can have two different meanings, both of which might apply: in the realm of computers or database transactions it denotes an atomic change of state, whereas in the realm of business or finance, the term typically denotes an exchange of economic entities (as used by, e.g., Transaction Processing Performance Council or commercial transactions.
A true fully (database, schema, and table) qualified query is exemplified as such: SELECT * FROM database. schema. table. Both a schema and a database can be used to isolate one table, "foo", from another like-named table "foo". The following is pseudo code: SELECT * FROM database1. foo vs. SELECT * FROM database2. foo (no explicit schema ...
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container.
A distributed database is a database in which data is stored across different physical locations. [1] It may be stored in multiple computers located in the same physical location (e.g. a data centre); or maybe dispersed over a network of interconnected computers.