What is meant by Data Integrity and what happens if there is lack of data integrity?
– Data integrity holds a lot of importance since it refers to the assurance and maintenance of the consistency as well as accuracy of the data over its whole life cycle.
– Also, data integrity is closely related to the relational data base management system.
– The validity, accuracy, and correctness of data ensured from any software or human errors or hardware failures is what that is demanded by the business intelligence and data ware housing.
– Data which is uniformly and properly integrated gets identical maintenance during operations such as storage, retrieval or transfer from one source to another.
– Data integrity is governed by the rules for the data retention which guarantee or specify the period of time for which the data can be retained by a particular data base.
– These rules also specify what should be done with the data when its validity has expired.
– For maintaining data integrity, it is important that these rules should be applied consistently and regularly to all the data retrieving systems.
– Any lack in the enforcement of these rules can introduce bugs and errors in data.
– If these rules are strictly enforced the error rates can be reduced, thus saving the time taken for trouble shooting and tracking the erroneous data.
– Rules that define the relationships a piece of data can have with the other data chunks are also defined under the concept of data integrity.
– Implementation checks and corrections of the invalid data that are carried out for maintaining the data integrity are based up on fixed schema or we can say a predefined set of rules.
– There are certain other rules called the rules of data derivation which specify how the data values are to be derived from a specific algorithm, conditions and contributors and also the conditions based on which the data can be re – derived.
– For the data integrity to be complete it is necessary that all the characteristics of data stated below must be correct:
1. Business rules
2. Rules stating the relations among the pieces of data
5. Lineage etc.
– Another important thing is that the functions being performed on the data (such as transforming the data, storing meta data, storing history and so on.) must ensure its integrity.
What are different types of data integrity constraints?
Following are the different types of data integrity constraints:
1. Entity integrity:
– This type of data integrity is mainly concerned with the concept of what is called a primary key.
– This is an integrity rule stating that the every table should possess a primary key and it is should be unique and should not be null.
2. Referential integrity:
– This type of data integrity is mainly concerned with the concept of foreign key.
– This is another integrity rule stating that the there can be one of the two states in which any foreign key value can exist.
– One state is that the foreign key should refer to the primary key value of the some table present in that particular data base.
– However, this depends on the rules of the business and therefore can be null.
3. Domain integrity:
– This type of integrity is concerned with the declaration of all the columns in a relational data base up on a domain that is defined.
– Data item is the primary unit of data in any relational model and is said to be atomic or non – decomposable.
– A domain consists of data values that are of same type.
– They can be therefore considered to be a pool of values from which the actual values can be drawn for the columns of the table.
Any data base supporting all these features holds the responsibility to ensure the integrity of the data as well as its consistency for retrieval and storage.