Database Normalization

An Overview of Database Normalization and Best Practice to Follow

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Database administration demands you to gain not only various technical skills to ensure proper setup and upkeep of a database, but one should also possess an understanding of the database best practices to ensure optimum database outputs. Database normalization is such a concept, which is a strategy used to organize a database properly into its form of tables and columns. The objective of database normalization is to maintain the table catering to a specific top with supporting topics too included. You may compare it with a spreadsheet which consists of info information as salespeople and a set of customers serving various purposes like:

  • Identify the salespeople.
  • List the customers of your company whom they call to sell a product.
  • Identify which all customers are under which all salespeople.
  • Identify at which phase of the sales funnel each customer is in.

If you tend to limit the table to a single purpose, you may also reduce the chance of data duplication within the DB. This may also help eliminate some of the issues stemming from the DB modification. However, this has some downsides too.

To achieve all these objectives without limiting your database’s scope, you may try to use some well-established rules. As you tend to apply such rules, new tables may be formed. There are three basic forms for most of the databases to adhere to. As these tables satisfy the corresponding database normalization, these also have lesser risks of modification anomalies and may be more focused on the topic’s purpose. Before you move to these principles, let us first be sure that you understand the same definition. 

Why database normalization?

As we have seen above, there are three primary reasons for normalizing a database. First among these is to minimize the scope of any duplicated data. The second is to avoid or minimize any modification issues, and the third is to simplify the queries. As we get through different states of database normalization, we may further discuss how each form of these may address various issues. However, we may investigate some of the data that have not got normalized and discuss some possible pitfalls to start with.  

Data duplication anomalies

Data supplication anomaly may end up in an increased need for storage and decrease performance level. With duplication, it also becomes very difficult to maintain any data changes. Say, for example, suppose if we move an office location to another place. To get the change reflected in the table, you have to update all the current location entries. If this is pretty larger, then you may have to execute hundreds of updates also get more information on Newsskook. This is called modification anomaly. Doing proper database normalization will fix it up. 

Insert anomaly

Many factors may not record until we get the information related to the entire row. For example, we cannot record a fresh sales office until we know the salespersons linked to it. This is because, to create a new record, we have to give it a primary key. In this case of office shifting, the primary key may be SalesPersonID, which we do not have. For any further services related to database administration, you may rely on RemoteDBA.com expert services for remote database consultation.

Update anomaly

In update anomaly, we have the same info in various rows. For example, if the office telephone number changes as it is being shifted to a new place, there may be many updates. If we do not update it in all the rows, then the inconsistencies remain.

Deletion Anomaly

Similar to insert and update anomalies, the deletion anomaly also works uniquely. The deletion of a given row may cause the removal of one or more data sets. For example, if someone in the company retires, then deleting the row about that individual may cause a loss of much relevant business information linked to that ID.

Sort and search issues

We consider normalization because it will make it easier to search for the sort of data you need based on the business needs. For example, if you have to search for a specific customer, then you may run a query like:

If the customers were just in one column in the DB, then the query may have been simpler. It is also important to consider whether you want to run a query that has to be sorted by each customer. To do the above, you may have to use three separate queries of ‘UNIO.’ You can also reduce or eliminate such anomalies by separating the data into tables. This will put the data into different tables to serve a unique purpose. The process of designing such a table is known as database normalization.

Database normalization forms

There are three basic forms of database normalization, as we discussed above. These are the first, second, and third normal forms. These are usually denoted as 1NF, 2NF, and 3NF.  Along with these, there are some additional forms, too, like BCNF, but you may count those as advanced and not necessary to be learned and used at the beginning.

Database normalization forms are so progressive, which means that to qualify for the third normal form, the DBMS table must satisfy the second normal form rules. In turn, the second normal should adhere to the first normal form to function. Let us see how.

  • 1st normal form (1NF): information is stored in the relational table where each column contains atomic values—no repeating columns of groups. 
  • 2nd normal form (2NF): The table is in the first normal form by default, where all the columns will depend on the primary key of the table. 
  • 3rd normal form (3NF): The table is in the second normal form where the columns are not dependent transitively on any primary key. 

It may take a little time and practice for the beginners to understand the concept of data normalization and ensure the compliance of various normalization forms. However, practicing data normalization from the first phase of your enterprise database administration is essential to ensure that your data is in proper shape. The analysis reports on those may be accurate and reliable.

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