For example, you can place your database on two separate computers. If one computer goes down, the same data is available on the other computer. Redundancy is definitively a copy, but the access to either version of the data is 1 to 1 exactly the same to you. The RDBM may choose one or the other based on different parameters such as how fast or whether the other computer network connection is down. The RDBM manages the copies in very consistent ways, the same problem I just mentioned about user names.
If the RDBM is asked to update the user name, it has to update all the versions all copies. You may want to read about Cassandra for fun things in that respect. Cassandra is the type of database used by Google which update data in a ring of computers with possibly heavy redundancy. And Indexes duplicate your data further. To continue with the user example, the name of the user could be used in an index. The Index would then look like this, for example:. And here we see that the duplication is actually necessary.
And if you see how you can handle that properly, without ever losing any existing data, you understand why duplication is difficult to deal with. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. In Fig. The term normalization means to make normal in terms of causing something to conform to a standard, or to introduce consistency with respect to style and content.
In terms of relational database modeling, that consistency becomes a process of removing duplication in data.
Removal of duplication tends to minimize redundancy and minimization of redundancy implies getting rid of unneeded data present in particular tables.
In reality, normalization usually manages to divide information into smaller, more manageable parts. The most obvious redundancies can usually be removed without involving math. Commercially speaking, the primary objectives of normalization are usually to save space and organize data for usability and manageability, without sacrificing performance.
This process can present a challenge and solved through trial and error. Additionally the demands of 1 intensely busy applications and 2 end-user needs can tend to necessitate breaking the rules of normalization in many ways to meet performance requirements.
Rules are usually broken simply by not applying every possible layer of normalization. Normal Forms beyond 3rd Normal Form are often ignored and sometimes even 3rd Normal Form itself is discounted. Yes, once again, somebody is telling you to normalize your schema. In addition to the benefits of normalization that are glorified elsewhere, a normalized schema is far easier to replicate. Consider a schema that is in first normal form 1NF meaning its tables contain redundant data.
If this table contains records for customers who work for Acme Tire and Rubber, then records will have to be updated when Acme Tire and Rubber changes its name to Acme Tire and Rubber and Lawn Furniture. Data corruption is when data becomes damaged as a result of errors in writing, reading, storage, or processing. When the same data fields are repeated in a database or file storage system, data corruption arises. If a file gets corrupted, for example, and an employee tries to open it, they may get an error message and not be able to complete their task.
Data redundancy may increase the size and complexity of a database — making it more of a challenge to maintain. When more data is created due to data redundancy, storage costs suddenly increase. This can be a serious issue for organizations who are trying to keep costs low in order to increase profits and meet their goals.
In addition, implementing a database system can become more expensive. Fortunately, it is possible to reduce unintentional cases of data redundancy that often lead to operational and financial problems. Master data is a single source of common business data that is shared across several applications or systems.
Although master data does not reduce the occurrences of data redundancy, it allows companies to work around and accept a certain level of data redundancy. This is because the use of master data ensures that in the event a data piece changes, an organization only needs to update one piece of data. In this case, redundant data is consistently updated and provides the same information.
Database normalization is the process of efficiently organizing data in a database so that redundant data is eliminated. By implementing data normalization, an organization standardizes data fields such as customer names, addresses, and phone numbers. Normalizing data involves organizing the columns and tables of a database to make sure their dependencies are enforced correctly.
When it comes to normalizing data, each company has their own unique set of criteria. For instance, one company may want to normalize the state or province field with two digits, while another may prefer the full name.
Regardless, database normalization can be the key to reducing data redundancy across any company. Efficient data redundancy is possible.
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