Database Schema: Definition, Types, and Benefits

Database schemas provide an overview of how databases are structured, including their elements, how they relate to one another, and the rules that govern them. As a result, they’re not only the cornerstone of an effective database design but also of efficient team-wide database management.  

In this article, you’ll learn more about what database schemas are, their benefits, and the different types you’ll likely encounter in the work world. You’ll also learn about two common schema designs, some best practices for schema integration, and explore flexible, cost-effective courses that can help you deepen your data knowledge today. 

What is a database schema?

A database schema is a blueprint that outlines a relational database’s architecture, describing how data is organized within it and how its different elements, like foreign and primary keys, data types, and fields, relate to one another. Typically, database schemas are visually represented using an entity-relationship diagram, which depicts how values are stored, their relationship to one another, and the rules governing them. 

The process of designing a schema is referred to as data modeling and is often performed by such data professionals as data architects, data scientists, and data analysts. A schema is housed within a database management system (DBMS) so that it can be referenced by programmers, administrators, and users and maintain the integrity of the overall system. 

Database schema benefits 

There are many benefits to using a database schema. Some of the most common include: 

• Database security. A schema can outline access permissions to certain parts of a database, allowing greater control over who sees what and why. Database administrators can then use this information to grant permission to those who require it. 

• Greater fidelity. A schema ensures that a database is properly maintained by those who use it. Among other things, this can greatly limit the number of duplicates and unnecessary information contained within a database. 

• Improved communication. A schema allows stakeholders to communicate with one another more effectively about how to use and maintain a database over time. This can significantly cut down on confusion and miscommunication. 

Database schema vs. Database instance

A schema acts as the blueprint for a database, describing its overall structure and how each element relates to another. In effect, it’s an unchanging picture of how the database is formally organized. 

A database instance, on the other hand, is a snapshot of the information contained within a database at a specific time. This means that, unlike a schema, the information contained within a database instance can change over time. 

Oracle database schema 

The term database schema has a unique meaning in Oracle that differs from its more widespread definition. 

In Oracle, a database schema specifically refers to “a collection of database objects,” such as tables, indexes, and views, and is owned by and named after individual database users [1]. Schema objects are user-created logical structures that can be manipulated using Structured Query Language (SQL) or Oracle Enterprise Manager. 

Types of database schema

There are several different types of schema used for databases. The three most common types you’ll likely encounter in the field include: 

• Conceptual schema. A conceptual database schema represents all the elements contained in a database and illustrates their relationship to one another, but it doesn’t contain any tables. As a result, it provides a big-picture view of the database without offering real-world details. 

• Logical database schema.  Logical schemas flesh out conceptual schemas with more concrete details about the objects that will be contained within them, such as names, tables, views, and integrity constraints. 

• Physical database schema. A physical schema is an actual design for a relational database. It includes all the technical and contextual information needed for the schema and is created with a specific physical data system in mind. 

Star and snowflake schemas

The two most common database schema designs are star and snowflake schemas. And, as their names suggest, their designs often look like visual representations of a star and a snowflake. 

A star schema is a simple schema design in which a single fact table is connected to one or more dimension tables. Also known as a “Star Join Schema,” this schema is simple to implement and particularly effective at querying large data sets. 

A snowflake schema, meanwhile, is a more complex version of a star schema in which a single fact table is connected to one or more tables, which themselves can be connected to other dimension tables. A snowflake schema is relatively easy to maintain and can perform more complex queries than a star schema, allowing for increased analytics possibilities. 

Schema integration requirements 

Schema diagrams help ensure that databases follow a consistent structure so that they can be used effectively by anyone accessing the database. When a database has multiple schemas, it’s important to ensure they are effectively integrated with one another. 

To ensure that database schemas are integrated, the following requirements must be met: 

• Overlap preservation. Overlapping elements from different schemas should be integrated within a database schema relation. 

• Extended overlap preservation. Elements from only one source associated with overlapping elements should appear within the database schema. 

• Minimality. To maintain integrity, none of the elements should be lost.  

• Normalization. Elements and relationships that are independent of one another shouldn’t be grouped together within the same schema. 

Learn more with Coursera 

Great database design begins with great database training. If you’re looking to expand your knowledge of all things data, consider taking a relevant Professional Certificate through Coursera today. 

In Google’s Business Intelligence Professional Certificate, meanwhile, you’ll expand your knowledge of data analytics with practical, hands-on projects featuring BigQuery, SQL, and Tableau.

In the Meta Database Engineer Professional Certificate, you’ll learn the key skills required to create, manage and manipulate databases, as well as industry-standard programming languages and software such as SQL, Python, and Django.