Introduction to Relational Data Modeling

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Relational data modeling is a fundamental technique used in modern database design and management. It is based on the concept of organizing data into tables that are related to each other based on common fields, allowing for efficient storage, retrieval, and analysis of data. The relational data model is widely used in a variety of applications and industries, from finance to healthcare to e-commerce.

In this article, we will explore the basics of relational data modeling, including its key concepts, advantages, and applications, providing a foundation for understanding this essential data management technique.

What is Relational Data Modeling?

Relational data modeling is a data management technique that has its roots in the work of Edgar F. Codd, a computer scientist at IBM in the 1960s and 70s. Codd developed the relational model for database management, which was a major breakthrough in the field of data management.

Prior to the development of the relational model, databases were managed using hierarchical or network models, which were rigid and inflexible. These models were difficult to scale and modify, and they required extensive programming to perform even simple tasks.

Codd’s relational model introduced a new way of organizing data using tables that were related to each other based on common fields. This approach allowed for more flexible data management, as well as improved querying and reporting capabilities.

In a relational data model, data is organized into tables, with each table representing a specific entity or object. Each table consists of rows (also called records or tuples) and columns (also called fields or attributes). The columns represent specific pieces of information about the entity, such as a customer’s name, address, and phone number.

Tables in a relational data model are related to each other through common fields, which are called keys. A key is a field that uniquely identifies each row in a table. By using keys, data in one table can be linked to data in another table, allowing for complex queries and data analysis.

The relational data model provides several advantages over other data models, including:

1. Flexibility: The relational data model allows for the easy addition, modification, and removal of data without impacting other parts of the system.
2. Scalability: The relational data model can easily handle large amounts of data and can be scaled up or down as needed.
3. Data Integrity: The relational data model enforces data integrity by requiring each record to have a unique key, ensuring that there are no duplicates or missing data.
4. Querying: The relational data model allows for complex queries that can retrieve data from multiple tables, making it easier to analyze and report on large data sets.

Different Keys with Different Purposes

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Keys are essential for maintaining data integrity in a relational database, as they ensure that each row in a table is unique and that relationships between tables are properly established. By using keys, data can be efficiently queried and analyzed, making relational data modeling a powerful technique for managing data.

There are several types of keys in relational data modeling;

1. Primary Key: A primary key is a unique identifier for a row in a table. Each table should have one and only one primary key. Primary keys are used to enforce data integrity and ensure that each row in a table is unique.
2. Foreign Key: A foreign key is a field in one table that refers to the primary key of another table. This creates a relationship between the two tables, allowing data to be linked and queried across tables.
3. Candidate Key: A candidate key is a field or set of fields in a table that could potentially be used as a primary key. Candidate keys must be unique and non-null, but they may not be the primary key.
4. Composite Key: A composite key is a primary key that consists of more than one field. This is used when a single field cannot uniquely identify a row, but a combination of fields can.

Below we can see the E-R diagram of the DVD Rental sample database. Based on the descriptions above;

• In the rentaltable rental_id column is the primary key which means rental_id column is the unique representation for rental table.
• In the rental table inventory_id , customer_id , and staff_id columns are the foreign keys which means these columns are the connectors to the inventory , customer , and staff tables.
• In the film_category table film_id and category_id columns are candidate keys which means we can use either or both of them to represent uniqueness in the film_categorytable.

Image courtesy by https://www.postgresqltutorial.com/postgresql-getting-started/postgresql-sample-database/

What is Normalization?

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Normalization is the process of organizing data in a database so that it is structured efficiently and avoids data redundancy. In the relational data model, normalization is important because it helps to eliminate data inconsistencies and improve data integrity.

The normalization process involves dividing large tables into smaller, more manageable tables and establishing relationships between them. This is done by identifying functional dependencies between attributes, which means determining which attributes are dependent on other attributes in the same table.

There are different levels of normalization, each with its own set of rules and criteria. The most commonly used levels of normalization are:

1. First Normal Form (1NF): In this form, each column in a table contains only atomic values, meaning that it cannot be further divided into smaller pieces. Each row in the table must also be unique.
2. Second Normal Form (2NF): In addition to meeting the criteria for 1NF, a table in 2NF must have all non-key attributes (i.e., attributes that are not part of the primary key) dependent on the entire primary key, not just a part of it.
3. Third Normal Form (3NF): In addition to meeting the criteria for 2NF, a table in 3NF must not have transitive dependencies, meaning that non-key attributes cannot depend on other non-key attributes.

The normalization process ensures that each table in a database contains only one type of data and that each piece of data is stored in only one place. This helps to prevent data inconsistencies and makes it easier to update or modify the data without affecting other parts of the database. It also improves data integrity by reducing the risk of data anomalies, such as duplicate data or incorrect data values.

Normalization example

Let’s say we have the initial table student_courses as below.

STUDENT_COURSES
----------------
Student_ID
Student_Name
Course_ID
Course_Name
Course_Teacher
Course_Duration

To convert this table into 1NF, we need to ensure that each column contains atomic values. We can do this by splitting the table into two separate tables;

STUDENTS
--------
Student_ID
Student_Name

COURSES
-------
Course_ID
Course_Name
Course_Teacher
Course_Duration

STUDENT_COURSE_ENROLLMENT
-------------------------
Student_ID
Course_ID

To convert this new table structure into 2NF, we need to ensure that each non-key attribute depends on the entire primary key. In this case, the primary key for STUDENT_COURSE_ENROLLMENT table is (Student_ID, Course_ID), and all non-key attributes (i.e., Course_Name, Course_Teacher, and Course_Duration) depend only on Course_ID. Therefore, we can split the COURSEStable further into two separate tables;

STUDENTS
--------
Student_ID
Student_Name

COURSES
-------
Course_ID
Course_Name
Course_Teacher

COURSE_DURATION
---------------
Course_ID
Course_Duration

STUDENT_COURSE_ENROLLMENT
-------------------------
Student_ID
Course_ID

Finally, to convert the table structure into 3NF, we need to eliminate transitive dependencies. In this case, we have a transitive dependency between Course_ID and Course_Teacher. To eliminate this, we can split the COURSEStable further into two separate tables:

STUDENTS
--------
Student_ID
Student_Name

COURSES
-------
Course_ID
Course_Name

COURSE_TEACHERS
---------------
Course_ID
Course_Teacher

COURSE_DURATION
---------------
Course_ID
Course_Duration

STUDENT_COURSE_ENROLLMENT
-------------------------
Student_ID
Course_ID

Conclusion

In the modern data stack and agile software engineering, relational data modeling plays a crucial role in ensuring high data quality and reliability. By organizing data into tables with clear relationships and constraints, relational data modeling facilitates data analysis and manipulation while minimizing errors and inconsistencies. This approach enables teams to work more efficiently and effectively, leveraging automation and data visualization tools to make better decisions.

Agile methodologies, which prioritize collaboration, iteration, and continuous improvement, further enhance the value of relational data modeling by ensuring that data models remain responsive to changing business needs and priorities. As organizations increasingly rely on data to drive decision-making, the importance of relational data modeling and agile software engineering will only continue to grow.