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Normalization is the procedure through which you break down and simplify the relations (tables) in a database to achieve efficiency in retrieving and maintaining data. The most common reason for normalizing table data is to avoid redundancy, which reduces data storage requirements and leads to more efficient queries. Another reason to normalize data is to avoid data anomalies.
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You ve probably heard discussions about normalization that range from treating it like the Holy Grail to viewing it as a feature that adversely affects performance. What is it about normalization that gets people going so You can put all your data somewhere in a table, and as long as you can write SQL code to retrieve the necessary data, and you have a good RDBMS running on a machine with plenty of fast processors, you shouldn t have a slow-performing database, right The truth is that poorly designed relations and tables in a database can have serious effects, not only on the efficacy of your database, but also on the validity of the data itself. Let s look at an example of an ordering system in a warehouse. Imagine a simple table with each customer s information contained in a single tuple or row. What happens if customer A has 1,000 transactions and customer B has only one or two transactions Either customer A s transactions will not all fit in the row, or customer B s row will be mostly empty. Either you will not be able
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C HA PTE R 2 RELATIONA L DATABA SE M ODELING A ND DATABA SE DES IGN
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to cater to the customer, or you will waste a tremendous amount of space in the database. Simple queries turn into terrible resource wasters under this design. You can try another variation on the previous design by creating a much more compact table by allowing repeatable values of the attributes. That is, for each transaction, each customer s complete information would be repeated. Now you have just traded one set of problems for another. If customer A s information changes, each of that customer s rows in the table would need to be updated. For such repeated groups, when you perform updates, you have to make sure to update all occurrences of the particular customer s data, or you will end up with an inconsistent set of data.
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You can see on an intuitive level that designing without a solid design strategy, based on sound mathematical principles, will lead to several problems. Although it is easy to see the inefficiency involved in the unnecessary consumption of storage space and longer query-execution times, other, more serious problems occur with off-the-cuff design of tables in a database these are the socalled data anomaly problems. Three types of data anomalies can result from improperly designed databases: The update anomaly: In this well-known anomaly, you could fail to update all the occurrences of a certain attribute because of the repeating values problem. The insertion anomaly: In this anomaly, you are prevented from inserting certain data because you are missing other pieces of information. For example, you cannot insert a customer s data because that customer has not bought a product from your warehouse yet. The deletion anomaly: In this anomaly, you could end up losing data inadvertently because you are trying to remove some duplicate attributes from a customer s data.
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Note The debate between database developers and designers continues over denormalization. Many believe it s okay to break almost all design rules and denormalize for performance gains. However, others believe that this isn t correct and that the act of denormalization reduces the integrity of the database by removing the controls that lie at the heart of RDBMS design.
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