In this reading, you’ll discover several common ways to organize your data, moving beyond the simple “one application, one database” setup. By learning these patterns, you’ll see how larger projects and organizations often structure their data. You’ll also find out how you can apply a specific pattern to your own portfolio projects, especially when deploying to services like Render.com.
Note: The examples use the SQL lowercase snakecasing convention (e.g., sql_table_name),
while in Sequelize you’ve been using an UpperCamelCase convention (e.g., SequelizeTableName).
The most basic pattern is to have a single database dedicated to a single application. This database contains all of the tables the application needs, and it’s often named after the application itself.
For example, suppose you have an online community with three tables: users,
posts, and comments. You might keep all three tables in a
single database named community:
community
└── users
└── posts
└── comments
This works well for small, stand-alone projects. All data is in one place, making it easy to manage.
As applications grow in complexity, you might split your data into multiple databases. This is common when a single frontend (client-side app) needs data from different services. Each service might have its own database.
For instance, you could have an e-commerce service running from one database, and a community or forum service running from another. Both can still be accessed by the same client application:
service_ecommerce_db
└── orders
└── products
└── ...
service_community_db
└── users
└── posts
└── comments
Separating data in this way can help secure each set of data individually and reduce the blast radius of any outage (if the community DB goes down, the e-commerce DB might remain operational).
In larger organizations, multiple applications may rely on the same pool of data.
For instance, you might have a shared users table that various internal
and external apps query. In such a scenario, you could store all the data in a single database,
which multiple apps can access:
central_db
└── users
└── orders
└── products
└── comments
...
This can be cheaper (one hosting instance) and more flexible (any app can read from these tables). However, you need strict guidelines to avoid naming collisions and accidental conflicts when multiple apps share the same database.
This pattern is similar to Pattern 3 but adds a layer of organization: each application’s tables live in a separate schema within a single database. Schemas help you group tables logically and avoid naming collisions. You can think of schemas as separate namespaces inside the same database.
big_db
└── schemaA
└── users
└── posts
└── schemaB
└── users
└── books
Each schema can have a table named users, but they won’t conflict because
they’re referenced like schemaA.users and schemaB.users.
SaaS providers commonly use this approach to host multiple instances of the same application
for different clients, each instance nested in its own schema.
For your portfolio projects, you’ll use Pattern #4. This approach allows you to run multiple projects on a single Render.com free-tier database. Each project will have its own schema, letting you keep them separate and well-organized.
You’ll encounter two main changes when doing this:
public schema by default, or you can create your own custom schemas
for each project.
Sequelize configuration updates so that migrations, models, and seeders
point to your new schema in Postgres. This means specifying your schema name when defining tables.
This architecture broadens your backend knowledge and prepares you for real-world deployments where multiple apps coexist on the same database.
Below is a quick recap of the four common patterns for arranging data in databases:
You’ll use Pattern 4 in your portfolio project deployments, leveraging schemas to keep your tables neatly organized in a single hosted Postgres database. This approach is especially helpful for efficiently managing multiple projects on free-tier hosting.