What is a database schema and how does it work?

A database schema is a logical framework that organizes data into tables and fields based on rules and relationships so it can be easily retrieved and updated. Logically grouping objects in a database using schema prevents inconsistencies, reduces redundancies, and enhances security and scalability. 

Data schema vs data structure 

A data schema is an abstract concept — a logical view of a database — related to how it is constructed, while a data structure is the actual formats used to store the data within it (files, trees, arrays, hash maps, linked lists, etc.). A data schema will typically contain numerous data structures.

Think of schema as the visual blueprint of a house, while data structures are the materials and methods used to build it. 

A data schema is a high-level framework focusing on the design concept for a database, complete with logical relationships and definitions. In contrast, a data structure refers to the low-level details of implementation: the elements that allow data to be stored and managed.

How does a database schema work?

A database schema works by defining the system design elements of a database and outlining how data is stored, accessed, and managed within it. 

Imagine an Excel sheet. If you insert new values or update fields in a table, its structure will be affected. This table is an example of database schema, also known as a flat model, due to its simple, two-dimensional display.

Other, more advanced database schemas work similarly but with much more logical frameworks. They have predefined rules, or constraints, that ensure data is always consistent and accurate, even when new tables and columns are added. 

Primary and foreign keys guide the relationships between these elements. A primary key works as a unique identifier in its own table and is referenced by a foreign key in another table, linking the two together and establishing a relationship.

For example, in a customer database, the ‘customerID’ is the primary key in the “Customer” table and then acts as a foreign key in the “Orders” table. This allows each order to be linked to a specific customer. 

Database schema dictates how different tables and dimensions relate to each other, enabling users to execute queries quickly and efficiently without running into errors, duplications, and redundancies. And unlike Excel, schema also uses database programming languages like Python and Java.

What are the different types of database schemas? 

Now, let’s look at the different kinds of database schema. Multiple schema styles and types can be used to store data. What you need to use is dependent on the data being inputted.

• Physical database schema: A physical database schema refers to the physical storage media and technical information used to create data structures 
• Logical database schema: Logical database schemas define entity relationships and how each table is linked together using keys, etc.
• SQL server database: SQL server databases are designed with Microsoft’s SQL software application. They are relational database management systems used for business processes like business intelligence.
• NoSQL server database: A NoSQL server is an example of a non-relational database, which features a non-linear structure suited to semi-structured documents

Database schema also refers to how schema is designed: the specific structures or models that organize data. Common database schema types include:

1. Star schema

A star schema has a single, central fact table acting as a hub for multiple dimension tables connected to it. This type of schema is great for storing and analyzing lots of raw data.

The “fact” at the core is a numerical metric or data point, such as revenue generated, order count, profit margin, or website visits. This fact is linked to dimensions that provide contextual details for business intelligence (BI) and analytics teams. 

A star schema de-normalizes data to make queries faster. Star schemas are a simpler schema type compared to snowflake schema, for example.

2. Snowflake schema

A snowflake schema is a more complex version of a star schema featuring a central fact table linked to numerous dimensions. The main difference is that these dimensions have their own dimension tables rather than being restricted to a single-star structure. 

The expanded system is more complex but has a normalized structure: everything is only stored once. This reduces data redundancies and duplications but at the cost of slower query performance. That said, on the flip side, it makes data discovery and identification much easier to manage.

3. Relational schema 

A relational schema doesn’t have a fact table. Instead, data is organized into tables with rows and columns. The relational model defines how these tables and columns relate to one another. It can organize data in an SQL server, for example, by defining the logical structure of everything within it: tables, columns, or data types. 

What are the benefits of a well-designed schema? 

There are many benefits of database schemas, some of which include:

1. Improved data quality

Schema enforces data integrity constraints and rules that prevent duplication. For example, primary keys (unique identifiers) and foreign keys (links between data) maintain relationships between tables and ensure data accuracy and consistency.

2. Enhanced performance

Schema optimizes database query execution and data retrieval. Data is organized into logical structures: everything has a clearly defined place and purpose. These relationships minimize data redundancy and processing time and boost database performance.

3. Increased scalability

Schemas have a modular design that can scale and grow as needed to handle larger datasets and higher query volumes. Data is divided into multiple tables, each of which can be independently managed, shared, and replicated.

4. Better data access and security

Schemas provide access controls for administrators to set permissions, adding a layer of security for sensitive data. A single schema can contain personally identifiable information (PII), for example, which can be restricted to authorized users.

5. Reduced maintenance costs

Schemas don’t need to be restructured or updated regularly. The relationships between table columns and logical groupings enable additions and modifications to be made without disrupting the rest of the database. This reduces long-term costs.

How to design a database schema 

Designing a database schema is an exhaustive, logical process. It isn’t something you can fast-track, especially during the formative stages when defining rules and relationships can make or break the project. 

Follow these six steps to design the best database systems for your business.

Step 1. Analyze database requirements

Database design needs to be guided by your project requirements. What do you want your database to achieve, and what will the owner gain from creating it? 

Next, document the major functional requirements for the database. What must it do to support the end goal while ensuring data is efficiently managed and updated? 

Consider factors like data organization, data retrieval and querying, data validation, security and access control, scalability, and performance. You should also engage stakeholders to gather all the data necessary for the project and determine who will have access to it.

Step 2. Identify entities and relationships 

Now you have all of your data, you can start listing key entities. Examples of entities include ‘customer’ and ‘order’. Each of these entities has attributes.

• Customer attributes include ‘name,’ ‘email’ and ‘phone number’
• Order attributes include ‘order date’ and ‘total amount.’‍

Data schema represents these entities and the relationships between them, so define everything clearly. 

Step 3. Choose a database model

Evaluate different database models based on the project requirements and entities. 

A relational model might work best if you have structured data with well-defined relationships, as these can be organized into simple tables, rows, and columns. Operational databases with customer and order records, for example, would suit a relational model.

In contrast, if you want to conduct business analytics on larger sets of complex data, snowflake schema may be more appropriate. The highly-normalized structure supports multidimensional analysis, which is great for detailed reporting and insights. 

Step 4. Create an ERD

An entity relationship model needs to be visualized using an entity-relationship diagram (ERD). You can generate an ERD using online tools such as Canva, which has a collection of templates to build from.

An ERD illustrates the relationships between entities and how information flows. The diagram should be easy to follow for stakeholders and non-technical employees.

Step 5. Apply normalization, assign keys, and define rules

Now, it’s time to implement the design. In your database of choice, apply a normalization schema including First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF) to ensure data is stored in only one place. This reduces redundancies and deduplication.

You also need to:

• Convert entities into tables and assign your primary and foreign keys
• Define relationships between tables: one-to-one, one-to-many, or many-to-many
• Add constraints such as UNIQUE and NOT NULL

Step 6. Test and refine 

Before you go live with a real database, test your schema with sample data and run queries to identify any issues. You should also simulate different workloads to see how the database performs under different loads and confirm that your keys and constraints maintain data accuracy and integrity.  

Armed with feedback, refine the schema and adjust relationships to balance the speed, performance, and scalability required for your project. 

Database schema design best practices

Putting solid database design fundamentals into practice will make it easier to design schema and create visual representations.

The old adage about failing to prepare means preparing to fail rings true for schema. Take the time to fully understand what the data is, who it’s for, and how it will enhance decision-making. This will give you the knowledge (and tooling) to create data structures that support the expected volume of queries.

The database design should also achieve as many goals as possible: eliminating duplicate data, establishing data accuracy and consistency, securing data, or meeting compliance. Don’t build in haste and then repent at leisure. It will cost you time, money, and resources.

When designing a database management system, remind developers to:

• Use clear and simple naming conventions (e.g., no hyphens or spaces)
• Use strong encryption methods for sensitive data
• Set up user accounts for employees who need access and request user authentication
• Document database concepts and design methods, complete with instructions and comment lines for scripts and triggers
• Apply normalization correctly, as overuse and under-use can cause issues
• Build out schema slowly after double-checking fields and how tables relate to each other

Summing up

Database schema represents an opportunity to overhaul your database structure so it’s reliable, efficient, and scalable. When schemas are designed correctly, you reap the rewards of a logical database that actively works in your best interests, not against them.

Data is organized, accurate, and consistent; you can make faster queries and gain better insights with peace of mind that the infrastructure is scalable and secure.

Ready to make the switch to a cloud-native storage solution? RecordPoint can help you migrate away from legacy systems and ensure critical data is properly categorized and compliant. 

Contact us today to see how our data lifecycle management services can transform your business. 

FAQs

What is an example of a database schema?

An example of a database schema is a customer relationship management system with a relational schema. The schema is used to build a customer and orders database complete with relevant tables, keys, and relationships.

What are the three types of database schema?

There are three primary types of database schema: conceptual, logical, and physical. Conceptual schema is the high-level framework featuring entities and their relationships, while logical schema is the actual structure like tables and data flows. Physical schema specifies how data is stored. This is the physical infrastructure, such as a data warehouse or data lake.

What is a schema in SQL?

In SQL, a schema is a logical structure of data. Schema creates groups of database objects on a Microsoft SQL server linked to a specific user. Schemas in SQL organize objects, enforce rules, and assign security permissions. 

What is the difference between a table and a schema?

Tables are building blocks used to piece the database together, while the schema provides the structure and logical rules to keep everything accurate, consistent, and accessible.