The Beginner's Guide to Database Relationships

No matter your business type, you probably collect vast amounts of data. And if you’ve ever tried to do something with it (such as analyze or visualize it), you probably know that in its raw form, data is pretty much useless.

Consider raw data as wheat, clean data as flour, and analyzed and visualized data as freshly baked bread. To go from unstructured bits of data to something that can be further used in BI tools, you need a vast number of processes and tools. One of them is something called database relationships.

Today, we’re going to show you what they are and why they matter in a tutorial on database relationships for beginners.

What are database relationships?

Database relationships are the associations between different tables in a relational database. In a relationship database management system (RDBMS or DBMS), data is organized into tables. The relationships between these tables determine how the data in one table is connected to the data in the other table.

To understand database and table relationships, and how they fit into data modeling and schema design, we need to define a few key terms first.

The primary key: the unique identifier for each record in a table, ensuring that each row in a table is uniquely identified.

The foreign key: this is the field in the second table that connects to the primary key in the first table. It determines the link between the two tables. The foreign key establishes referential integrity, making sure that the relationships between two tables are valid.

Referential integrity: this ensures that the fields in the table from the foreign key correspond to fields in the table from the primary key. If the foreign key does not correspond to an appropriate primary key or the value is null, the referential integrity is violated.

Normalization: database relationships play a huge part in data normalization. They help organize the data in such a way that redundancy and dependency are reduced.

Important note: there are many different ways to model your data and build relationships in a database. However, not all of them are ideal for an analytics use case. The database relationships we’re about to show you are ideal for production databases and building a great foundation for your app.

For analytics use cases, such as embedded analytics, this is not the ideal structure because you would need a single dataset for all operations.

Why are database relationships important?

If you do database design and management and you want to prepare data for future analysis and visualization, database relationships should be of utmost importance to you. Here are a few key reasons.

Data integrity

Database relationships define the rules and constraints for data, which ensures integrity and maintenance of relationships between tables. With higher data integrity, there is a lower risk of data inconsistencies or orphaned data.

More efficient data retrieval

When database relationships are defined, the database joins related tables. This means that you can show and combine data across different tables in one result set. Consequently, this means faster querying and no redundancy when you store data.

Normalization

Database normalization is a key part of preparing data for further analysis and visualization. Having relationships between databases fosters normalization as larger tables are broken down into smaller ones which are easier to store and maintain.

Flexibility in querying

When relationships are established between database tables, it’s easier to retrieve specific information by searching through related tables. This also makes it effortless to extract more meaning from your data.

Scalability

The bigger your relational database, the more important it is to have established relationships. When ground rules are clearly set up, you can add large data volumes without changing much of the data structure.

Simplified maintenance

Want to make changes to one child table without affecting the entire database? You can do that with proper database relationships, which speeds up querying and makes your entire database more modular. Utilizing Liquibase, you can ensure seamless management of these relationships, allowing for precise adjustments to individual tables while maintaining the integrity of your database architecture.

Enforcing business rules

By default, database tables with clearly defined relationships enforce business rules. But once you set them up, they are set up for good, which means that new data has to meet certain conditions and requirements to even be added to the database.

The different types of relationships

There are three main types of relationships between tables in databases: one-to-one, one-to-many and many-to-many. To best understand which relationship type is being used, you first need to understand the data, the tables, and the business rules behind them. 

If you’re creating tables in a database like MySQL or SQL Server, you can define these relationships when building tables, using primary and foreign keys.

One-to-one database relationship

In this type of relationship, tables on both ends have only one record and every primary key value relates to none or one record in the related table.

One-to-one relationships are commonly forced by business rules and they don’t flow naturally from the data. These types of relationships are not very common in practice for one reason: it may be more practical to combine the two tables into one.

Example: two sets of tables. Table A has customer names and table B has customers’ contact information. Each entry in the first table corresponds to only one field in the second table.

One-to-many database relationship

Every record in the first table can have multiple corresponding records in the second table. However, every record in the second table corresponds to only one in the first table. A one-to-many relationship is the most common type of database relationship in modern databases.

Example: one database table has basic information about your customers. The other database has their orders. The orders table can have multiple orders for one customer. In the customers table, those multiple orders can only be linked to one customer at a time.

Many-to-many database relationship

Many-to-many relationships are the most complex of the three. In this case, every record in the first table can have multiple corresponding records in the second table. To use many-to-many relationships, it’s necessary to introduce a third table, called a junction table or linking table.

The third table connects the two tables and helps the relational database function smoothly.

Example: the first table contains students and their names, while the second table contains their courses. One student can attend many different courses. One course can have many different students.

Why this structure does not work for analytics use cases

If you want to create an embedded analytics dashboard, using database relationships is not such a good idea. 

When data is normalized, it’s spread across different tables, which ensures consistent definitions. For business users, this means that they’ll often have trouble understanding or using all of this data. There are just too many options to choose from.

The second problem is that there is a vast number of unpredictable options to analyze. This means that your database optimizers won’t be able to keep up with query complexity. The end result? Your analytics dashboards will take ages to load for your end users.

For an analytics use case, a better option is something called a star schema. This involves one large “fact” database with e.g. orders. This table has relationships with several “dimension” tables, e.g. shops, products, customers, and others.

On top of that, we typically recommend customers to further denormalize their database structure into a single dataset.

This ensures your dashboards load quickly and that your end-users know what data they have and what they can do with it. If you want to learn how to create a great structure for an analytics use case, this is a good resource to get started.

Wrapping up

Understanding database relationships is key if you want a clean relational dataset that can be further used for business intelligence. With clean and properly structured data, you can have better data integration, more accurate and structured reporting, efficient data retrieval, and overall better data quality.

And this is where we step in. If you want real-time, accurate insights from your data, you can visualize it in Luzmo, using denormalized data for quick insights. We help SaaS companies turn datasets into valuable insights for their customers, directly embedded inside their applications.

Book a free demo today so we can tell you more!