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Before an Oracle database changes data in a datafile it writes changes to the redo log. If something happens to one of the datafiles, a recovery procedure can restore a backed-up datafile and then replay the redo written since backup-time; this brings the datafile to the state it had before it became unavailable.
LNS (log-write network-server) and ARCH (archiver) processes running on the primary database select archived redo logs and send them to the standby-database host, [7] where the RFS (remote file server) background process within the Oracle instance performs the task of receiving archived redo logs originating from the primary database and ...
The "data scrubbing" operation activates a parity check. If a user simply runs a normal program that reads data from the disk, then the parity would not be checked unless parity-check-on-read was both supported and enabled on the disk subsystem.
The database consists of a collection of data files, control files, and redo logs located on disk. The instance comprises the collection of Oracle-related memory and background processes that run on a computer system. In an Oracle RAC environment, 2 or more instances concurrently access a single database.
A data file is a computer file which stores data to be used by a computer application or system, including input and output data. A data file usually does not contain instructions or code to be executed (that is, a computer program). Most of the computer programs work with data files.
The SGA can be said to consist of linked granules. The granule size depends on the database version and sometimes on the operating system. In Oracle 9i and earlier, it is 4 MB if the SGA size is less than 128 MB, and 16 MB otherwise. For later releases, it is typically 4 MB if the SGA size is less than 1 GB, and 16 MB otherwise.
Oracle Database provides information about all of the tables, views, columns, and procedures in a database. This information about information is known as metadata. [1] It is stored in two locations: data dictionary tables (accessed via built-in views) and a metadata registry.
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]