What is Database Functional Dependency?
Learn more about database functional dependency based on relational databases and the importance of database management systems.
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The digital era we live in today is a time of excess in many ways. The explosion of the internet defied all expectations and its continued rate of expansion is practically unfathomable. According to Forbes, 2.5 quintillion bytes of data are created on the internet every day with over 90 percent of the data in the world being generated over the last two years. To put it mildly, that’s a lot of data.
Terms like Big Data have become household due to the prevalence and massive quantities of data that we’re seeing today. For businesses, data is an incredibly valuable asset. Business data can be in the form of market research, customer histories, and financial performance. Regardless of what kind of data is being tracked, all of it is stored in a database.
If data is an invaluable asset, then databases are the equally valuable tools we use to store, retrieve, and analyze that data, turning characters and digits into actionable pieces of information. IT organizations rely on these databases for a vast array of purposes which is why the management of these systems is so imperative for the overall success of modern organizations. The system used for handling databases is called DBMS.
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What is DBMS?
Database Management System (DBMS) is a collection of software applications created to define, retrieve, manipulate, and manage the data within a database. Database administrators use DBMS to analyze and modify databases to maintain the quality and optimization of the database.
While data can be an incredibly valuable asset for companies today, the sheer volume of data available makes it a complicated and costly resource to manage. Strong DBMS is essential for modern businesses to compete with the need for DBMS solutions rising as the company grows in size. There is a strong relationship between an organization’s success and its ability to manage its data.
Databases seem fairly straightforward at a glance, but there is a lot more going on in the world of DBMS than you might initially assume. Over time, there have been many different types of databases:
- Object-Oriented Databases organize data in a way similar to object-oriented programming concepts by creating objects that contain data and methods within them.
- Hierarchical Databases which organize data into trees of parent records that have related children records branching off of them.
- Network Databases organize data similar to hierarchical databases but with the added possibility of entries having multiple parent records.
- Relational Databases use tables to organize data by entry names that hold information within them.
Relational databases are the most commonly used databases by modern organizations due to their flexibility and power.
As mentioned, relational databases employ the use of tables to organize the data. However, it’s not really the table that makes the so powerful; it’s the ability for rows within the tables to become linked to rows in other tables. This is where the relational part of the database comes into play.
Within tables, each row has a unique “key” that is used as an identifier for that entry and these keys can be referred to by entries within other tables in the database, creating a relationship between the separate entries. The use of a key is known as a functional dependency.
What is a Functional Dependency?
Functional dependencies (FDs) establish the relation of one attribute to another attribute within a database. The use of a unique key field is one of the primary ways in which functional dependencies are practiced within databases. Utilizing a functional dependency allows for associations to be made to the correct entity even if other data points within an entry are identical to a separate entry.
Functional dependencies are often denoted with the use of an arrow →. For instance, A → B indicates that A functionally determines B. A good example of this is to consider social security numbers (SSNs). SSNs are unique identifiers for individuals in the United States and are used as a key to identify people who may otherwise be easily confused inside a database. You could say (SSN → birth date) because every unique SSN has a functionally dependent birth date associated with it.
However, the opposite (birth date → SSN) is not true because there are many people with the same birth date but only one person exists per each unique SSN. This concept is incredibly important for relational databases, serving as the foundation for how they are managed and manipulated using DBMS.
Functional Dependency in DBMS Example
Let’s look at a functional dependency example. A table called Customer Info might contain an entry that consists of a name and age for a customer. A unique key would need to be created for each new customer entered into this table because of the possibility for there being multiple people with the same name and age. While multiple people could have the same name and age, they could not have the same key.
Now, let’s say there is a separate table called Customer Orders where data for customer purchases is stored. The Customer Orders table would have an entry made into it every time a purchase was made and that entry would create a relationship between the order and the customer placing the order by using the key created in the Customer Info table.
This interdependency ensures that each unique customer is linked to their purchases even if there were multiple customers of the same name and age. Furthermore, because each entry in the Customer Orders table is linked with a Customer Info key, multiple orders associated with individual customers could be entered into the table.
Because of the massive amount of data available to modern organizations, the appropriate management of databases is imperative to ensure resources are put to good use and the data doesn’t go to waste. A bloated or otherwise poorly maintained database can be the source of a large number of headaches. Inefficient databases take much longer to analyze and sort through and bad data can lead to the making of poorly informed decisions.
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