Data inconsistency exists when different and conflicting versions of the same data appear in different places. Data inconsistency creates unreliable information, because it will be difficult to determine which version of the information is correct. (It's difficult to make correct - and timely - decisions if those decisions are based on conflicting information.) Data inconsistency is likely to occur when there is data redundancy. Data redundancy occurs when the data file/database file contains redundant - unnecessarily duplicated - data. That's why one major goal of good database design is to eliminate data redundancy. In the below link you can find more details. http://opencourseware.kfupm.edu.sa/colleges/cim/acctmis/mis311/files%5CChapter1-Database_Systems_Topic_2_Introducing_Databases.pdf
# Do you need random access to the data? # Does the data need to be ordered? # Can there be duplicate entries? # Are you more interested in reading from or writing to the data structure? # Are you more interested in data access speed or data storage size?
There is a school of thought residing with how much redundancy should be built in. obviously more redundancy should make it more reliable however those same additional parts could make the system fail as a consequence of additional redundancy.
It depends on what you are doing. The cyclic redundancy check will only detect an error, while the hamming code can also correct many types of errors. However to perform this correction the extra error detection parity bits required in hamming code are many more than the bits needed for cyclic redundancy check, per data byte being checked. Normally cyclic redundancy check is done on large block of data that can be resent or retried to get the correct block of data (e.g. telecommunication channels, disk sectors). Normally hamming code is done on individual bytes or words of computer memory.
Data is stored in databases. To make the database more efficient, different types of data are usually classified as a certain 'data type'.
redundancy
Storing the information several time leads to waste of storage space is called data redundacy. Data redundancy is a term used about databases and means simply that some data fields appear more than once in the database. Data redundancy is wasteful and inefficient for several reasons and database designers attempt to eliminate it as far as possible by using a technique called data normalization. Data redundancy occurs in database systems which have a field that is repeated in two or more tables. For instance, in case when customer data is duplicated and attached with each product bought then redundancy of data is a known source of inconsistency.
In database we store data however the can be redundant. Redundancy means repetitive data that is taking extra storage space . So to reduce or prevent the storage space we should eliminate redundancy or just reduce it.
with data redundancy there willbe more wastage of memory space as same type of data willbe saved many times when to want to see the data all duplicate results will come
Data redundancy refers to repetitive data in the database. In a system with redundant data it is difficult to manage the relationships. Data redundancy is the result of poorly designed database. By implying proper constraints on the data it can be prevented.
Data redundancy can lead to storage inefficiency, as the same information is stored multiple times. It can also increase the risk of inconsistencies or errors in the data, as updates may not be made consistently across all duplicates. Additionally, data redundancy can make data management and maintenance more complex and time-consuming.
Data redundancy: Sometime Data redundancy refers to in computer data storage, is a property of some disk arrays which provides fault tolerance, so that all or part of the data stored in the array can be recovered in the case of disk failure. The cost typically associated with providing this feature is a reduction of disk capacity available to the user, since the implementations require either a duplication of the entire data set, or an error-correcting code to be stored on the array.Redundancy is attained when the same data values are stored more than once in a table, or when the same values are stored in more than one table.One of the biggest disadvantages of data redundancy is that it increases the size of the database unnecessarily.
Data redundancy can be reduced by normalizing the database to eliminate duplicate data, creating relationships between tables, and using foreign keys to link related information. Using data validation rules and constraints can also help prevent redundant data from being entered into the database. Implementing a master data management strategy can centralize and standardize data, reducing redundancy across different systems.
Storing the same data in more than one place is known as data replication. This practice is often employed to improve data availability, reliability, and performance in distributed systems by ensuring redundancy and minimizing the risk of data loss in case of failures. However, it can also introduce complexity in data synchronization and consistency.
DBS has more security and data integrity.It reduce data redundancy and updating errors which can occur in FBS.Contains of concurrent data access.But also it's expensive to use and they are also complex.Damage to DB affects virtually all application programs.
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.
In RAID 1, write access is generally slower than read access. This is because data must be duplicated on two drives during a write operation, which requires more time than reading from either drive, where data can be accessed simultaneously. In contrast, read operations can take advantage of both drives, allowing for faster data retrieval. Thus, while RAID 1 offers redundancy for data protection, it does not enhance write performance.