If you have been working with big data for a while, you will no doubt be aware of data normalization techniques. By definition, data normalization is a database design technique that enables a database designer to make a logical database.
Through data normalization methods, a database designer organizes data into a wide range of related tables to minimize redundancy, especially when putting additional files. You can discover how to learn data normalization by reading several data normalization ideas.
Likewise, you can enroll in data science bootcamps and online courses, and you will almost certainly come across and use data normalization in your project. Below, we’ll go over the best data normalization techniques and how to apply them.
Best Data Normalization Technique Examples
Data normalization is a technique to reduce redundancy or duplicate data in a database. This ensures that only relevant data is stored in the complete table. This is done by analyzing and redesigning the original table to ensure that only the key constraint and the domain constraint remain.
The technique is a comprehensive evaluation of an unnormalized form of the table for better database results. Data normalization is a complex and difficult task so you have to learn how to manage databases for the design to perform efficiently.
Decimal Place Normalization
Decimal place normalization is typically applied to data tables containing numbers or numerical data types. This data normalization technique is quite simple; all you have to do is decide how many decimals you want in your database by scaling through the original table. This technique is often used when working with Excel.
Data Type Normalization
Data type normalization is another easy data normalization technique. This is used in the normalization design of types or subtypes of numerical data. For example, when you build a complete table in Excel, you will encounter numerical data that Excel recognizes as different types. These types may not respond correctly to formulas.
To minimize this problem, you have to use the data type normalization technique. When dealing with numerical types in the normalization design, experts recommend using the default comma style because it is the easiest to read.
Formatting Normalization
Formatting is an easy normalization technique if you are dealing with texts in the normalization design. While formats per se will not hurt your database, they can be distracting and may prevent a database designer from catching more errors or redundancies in your input sample in a table fragment. This normalization technique can be used in separate tables.
Z-score Normalization
The Z-score normalization is one of the most advanced data normalization techniques. It is used when dealing with numerical values with significant differences in size in the normalization design.
Using statistical methods, this technique normalizes the values to make it easier for a database designer to navigate the data. It also makes it easier for the database designer to input additional files into your database design.
Linear Normalization
Linear normalization is one of the most flexible normalization techniques. It establishes a new “base” of reference for each data point in the normalization design. It is also called “max-min” normalization, which means that you take the difference between the maximum and minimum x value in the data set to establish a base. It is usually done with the aid of statistical methods.
Clipping Normalization
Clipping is a tool that data scientists or database designers use after a normalization technique. It is mostly used to establish the maximum and minimum values of a dataset. Note that clipping does not remove points from a data set but reassigns data in the original table of data during the normalization design process.
Standard Deviation Normalization
To normalize data sets using the standard deviation technique, you divide the data by the standard deviation in the normalization design. This means that you make the standard deviation constant and thus remove the redundancies from the complete table. A standard deviation is an average difference between each data point and the average of those data points.
How to Normalize a Database
- Determine Your Data Source. The first step in normalizing a database is to identify the data source from which you will normalize. This could be accessed through the use of a data collection tool and statistical methods. You can now create the original table or the unnormalized form using the data source.
- Identify All Repeating Groups. Once you have your data source and have entered it into a complete table, you must now identify the repeating groups. Ensure that all data entries are atomic of a single value. It must also have a unique name. This is called the first normal form or 1NF.
- Make a Separate Table for the Single Value Data and the Repetitive Data. To avoid deletion and update problems, you have to input each data on a separate table. For example, if your data source is about students and each record shows that they have the same subjects and departments, you need to have a separate table for the student’s names, subject, and the departments.
This method will enable interactive updates. Through the separate tables, you will have the opportunity to update the dataset without deleting or accessing each table fragment. You can just access one complete table to ensure that the database is not in the unnormalized form. - Identify the Primary Key. Every record must have a unique identifier. This will be your primary key. A primary key constraint uniquely identifies each record on the table. Through the primary key, you will now have the functional dependency of your data. Functional dependency means that your attributes depend on the primary key column.
- Transform Your Table to the Second Normal Form or 2NF. Through the primary key, you can reduce the partial dependency by nominating another primary key for separate tables. This is called a candidate key.
Through this, you can easily find data with the given values in the candidate key column and identify the partial dependencies of non-prime attributes on candidate keys. If you have no candidate key, your database is in the unnormalized form. - Transform Your Table to the Third Normal Form or 3NF. Determine whether your values have a transitive dependency or if some values are not dependent on your set candidate key. To avoid this, place the values in a separate table. When the dataset in the complete table has a functional dependency, you have reached the third normal form.
- Transform Your Table to the Boyce-Codd Normal Form. A table should be in the third normal form to be in the Boyce-Codd Normal Form. You should also identify a super key that does not rely on non-prime attributes on candidate keys. To accomplish this, you must divide the complete table into two parts: one with the super keys and the other with another attribute on which the primary key is dependent.
Data Normalization Ideas: Top 5 Tips to Master Data Normalization
Data normalization may seem daunting, especially if you are going to analyze and design relations in your database schema. However, there are many data normalization techniques and there are also several ways to master them. Here are five tips on how to ace being a database designer through mastering data normalization.
Understand Your Data
The very first thing that you need to do before using a database design technique is to understand the data you are dealing with. This allows you to understand the possible changes to your data or if you have an additional file.
It will help in visualizing the normalization design that you will create. You will also know which statistical method to use for better performance and better data transformation from the unnormalized form.
Make Sure You Have All the Data You Need
The most frustrating aspect of designing a database is discovering that you have insufficient data and having to insert an additional file halfway through the process. To avoid this, make sure you have a complete table of data and know where it came from before putting it in a table fragment. This also helps in easily determining the candidate key needed for the design when transforming an unnormalized form into the first normal form.
Use Cluster Analysis
Grouping your data in clusters of related fields will help when you put each data in a separate table and a table fragment. The clusters will represent the future tables as well as the original table for your design and normalization technique. After this, you should consider how each separate table is related to one another for you to assign the candidate key.
Use Queries
When it comes to database design and data normalization, you must consider how you will analyze and manipulate the data. This is where queries come into play. You must understand how to use them to connect the data across each table fragment. The candidate key that you will nominate should also respond to queries and be flexible when you input an additional file.
Make Use of a Separate Table to Store Unique Information
Data normalization is the process of creating a database that allows you to quickly access the information you need. To accomplish this, store the same data in a single location, and assign a candidate key to it. Similar to the preceding tip, it will be beneficial to use cluster analysis when separating tables and to give each table fragment a name for easier navigation.
Are Data Normalization Techniques Worth Learning?
Yes, data normalization techniques are worth learning to reduce redundancy in your database. If you do not reduce the repetitive data, it will lead to problems with insertion, deletion, and problems with field updates. Data redundancy will lead to inconsistent or missing data. Data normalization is one of the most important data science processes that a data scientist needs to know.
To know more about data normalization techniques, you can attend data science events to learn from experienced data scientists. You can start your data science career by learning the tricks of the trade through these events.
Data Normalization Techniques FAQ
Yes, there are a few disadvantages to data normalization. One of these is the increased amount of time you must devote to the database. The more tables there are to join, the more time it takes. Another issue is the difficulty in normalizing data.
Yes, you can denormalize data. Denormalization involves adding redundant data or a table fragment to improve database read performance at the expense of write performance. This means reverting the table to its original state by combining two relations into one.
The most widely used form of normalization is the first normal form. You will not be able to normalize data without transforming the original table to the first normal form. During the first normal form, each data entry in every table fragment must have only one value for each cell.
A database developer is responsible for data normalization. A database designer oversees the designing, developing, executing, and preserving of the company’s data management system. If you are interested in becoming a database developer and quickly joining the job market, consider enrolling in a bootcamp program.
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