In non-database systems each application has its own private files. This can often lead to redundancy in stored data, with resultant waste in storage space. In a database the data is integrated. The database may be thought of as a unification of several otherwise distinct data files, with any redundancy among those files partially or wholly eliminated. Data integration is generally regarded as an important characteristic of a database. The avoidance of redundancy should be an aim, however, the vigour with which this aim should be pursued is open to question. Redundancy is * direct if a value is a copy of another * indirect if the value can be derived from other values: ** simplifies retrieval but complicates update ** conversely integration makes retrieval slow and updates easier * Data redundancy can lead to inconsistency in the database unless controlled. * the system should be aware of any data duplication - the system is responsible for ensuring updates are carried out correctly. * a DB with uncontrolled redundancy can be in an inconsistent state - it can supply incorrect or conflicting information * a given fact represented by a single entry cannot result in inconsistency - few systems are capable of propagating updates i.e. most systems do not support controlled redundancy.
Database is collection of some inter related records . And yes, data redundancy be completely eliminated when database approach is used.
Redundancy means duplicacy of data or repetitive data. In distributed database case the data is stored in different systems . So the answers is yes there can be redundancy of records / data.In distributed database , data is stored in different systems. Since the data is distributed there is redundancy of records.
Memory should be taken into account when building a database and maintain integrity and avoid redundancy through normalization.
Redundancy is when you have the same data in multiple locations. Some redundancy is good, while too much is bad. If two departments are using the exact same data, then this redundancy is bad. It is utilizing excess resources. Redundancy, can be used as a failsafe. Having a backup helps incase of data corruption. The key is too find the right balance of redundancy within a database.
Dis Advantages: Security may be compromised without good controls, Extra hardware may be required, System is likely to be complex Advantages: Reduced data redundancy, Secured data, Integrated data, Controlled data inconsistency
Controlled redundancy refers to intentionally duplicating certain components in a system to ensure reliability and fault tolerance, whereas uncontrolled redundancy occurs unintentionally due to inefficient processes or lack of coordination. Controlled redundancy is planned and managed to enhance system performance, while uncontrolled redundancy can lead to inefficiencies and waste of resources.
it is the process of finding the redundancy.
Database is collection of some inter related records . And yes, data redundancy be completely eliminated when database approach is used.
Advantages provided by a database system are : a) Redundancy of data is reduced. b) Secured data. c) Controlled data inconsistency. d) Integrated data. e) Standardized data
Avoid redundancy, for instance.
Redundancy means duplicacy of data or repetitive data. In distributed database case the data is stored in different systems . So the answers is yes there can be redundancy of records / data.In distributed database , data is stored in different systems. Since the data is distributed there is redundancy of records.
Database Normalization is the process of organizing the fields and tables of a relational database to minimize redundancy and dependency
this is true!
its called data redundancy.
data redundancy
A database is a collection of interrelated data and the advantages of a database are ensured efficiency, standardized data, maintainable data, integrated data, reduced redundancy of data.
Data redundancy in DBMS refers to the duplication of data within a database system. This can result in inconsistencies and inefficiencies, as well as consuming more storage space. It is important to minimize data redundancy in order to maintain data integrity and improve performance.