Database schema design is a strategy for constructing a framework for data management. Just like in architecture, a solid database needs to have a blueprint to keep the project on track. 

    A database schema design is like a blueprint for massive amounts of data. The schema is a skeleton structure that represents the logical view of the database as a whole. By defining categories of data and creating relationships between those categories, database schema design makes data much easier to consume and interpret. This article will offer an overview of how database schema design works, as well as examples and best practices to help you optimize your databases.

    Table Of Contents

    1. The Importance of Database Schema Design
    2. How do Companies Use Database Schema Designs?
    3. What do Database Schema Designs include?
    4. Best Practices and Use Cases for Schema Design
    5. Conclusion

    The Importance of Database Schema Design

    Databases store all the important data needed to run software applications and systems. There is always at least one database that is working at all times to keep applications up and running. 

    But the amount of information contained in a database without the ability to break it down and analyze it. Inefficiently organized databases suck up tons of energy, tend to be confusing, and are hard to maintain and administer. That’s where database schema design comes into play. 

    Database schema design organizes the data into separate entities, determines how to create relationships between organized entities, and how to apply the constraints on the data. 

    Database designers create the schema to give programmers and analysts a logical understanding of the data, making it easier to retrieve, manipulate, and produce information. 

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    How do Companies Use Database Schema Designs?

    A database schema design can exist both as a visual representation and as a set of formulas, or use constraints, that govern a database. Developers then express these formulas in different data definition languages, depending on the database system you’re using. Even though the leading database systems have slightly different definitions of what schemas are, MySQL, Oracle Database, and SQL Server each support the CREATE SCHEMA statement. 

    Database Schemas outline the architecture of a database and help to ensure the following:

    • Data entries have consistent formatting
    • All record entries have a unique primary key
    • No omission of important data

    For example, let’s say you are creating schemas for different departments. Analysts in each department would have access to the department’s schema account. The Accounting analyst would create tables and views inside of the Accounting schema. The analyst could then offer other team members access to read a table that lists employee expenses per period, Employee ID Numbers, etc. Another table might list employee salaries. Analysts can determine which roles and users can read, write, or edit the data in specific data sets within the database. 

    What do Database Schema Designs Include?

    We can broadly divide database schemas into two categories:

    • Physical Database Schema: The Physical database schema refers to how data is stored physically on a storage system and the form of storage used (files, indices, etc.). It dictates how you will later store the data. The physical schema helps arrange the data in a clear and logical fashion by defining all of its attributes. 
    • Logical Database Schema: The logical schema outlines all the logical constraints applied on the data and defines fields, tables, relations, views, integrity constraints, etc. These requirements provide useful information that programmers can apply to the physical design of the database. The rules or constraints, defined in this logical model help determine how the data in different tables relate to each other. 

    The definition of physical tables in the schema comes from the logical data model. Entities become tables, the entity’s attributes become table fields, etc. 

    Returning to our accounting example, a specific schema might contain the structure of two tables: 


    Title: Users

    Fields: ID, full name, email, DOB, department

    Table 2:

    Title: Overtime Pay

    Fields: ID, full name, Time Period, Hours Billed

    There are a few pieces of information in this single schema, including the titles of Table Names, the Fields each table contains, relations between tables (i.e Overtime Pay links to a User), and any additional info that is relevant. Developers or administrators then convert these schema tables into SQL code. 

    Best Practices and Use Cases for Schema Design

    In order to make the most of database schema design, it’s important to follow these best practices to ensure that developers have a clear point of reference about what tables and fields a project contains, etc. 

    • Define and use appropriate naming conventions to make your database design schemas most effective. 
    • While you may decide on a particular style or else adhere to an ISO standard, the most important thing is to be consistent in your name fields. The following tips will also help you effectively structure your schema.
    • Try not to use SQL Server reserved words in table names, column names, fields, etc. because it is likely to deliver a syntax error. 
    • Don’t use hyphens, quotes, spaces, special characters, etc. because they will not be valid or will require an additional step. 
    • Use Singular for table names (i.e. use StudentName instead of StudentNames). The table represents a collection, so there’s no need to make the title plural. 
    • Omit unnecessary prefixes or suffixes for table names (i.e. use Department instead of DepartmentList, TableDepartments, etc.)
    • Keep passwords encrypted. 
    • Don’t give admin roles to each user - request authentication for database access. 
    • Document database design with schemas and instructions. Write comment lines for scripts, triggers, etc. 
    • Use normalization as required to optimize performance. Both over-normalization and under-normalization will result in worse performance. 

    Understanding your data and the attributes of each element helps you build out the most effective database schema design. A well-designed schema can enable your data to grow exponentially. As you keep expanding your data, you can analyze each field in relation to the others you are collecting in your schema. 

    Once you have a clear understanding of this process, you can keep adding data from different sources. The goal is to bring in as many elements of your database as you can and organize them into logical groupings that your team can absorb and interpret. 

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    Need Help With Your Database Schema Design?

    Database schemas help administrators and developers understand the intricate structure of your database so they can keep building on it and managing it effectively. Hopefully, these guidelines and best practices will help get you started on the right path in your database schema design. If you need help designing or updating your database management system, schedule a demo with Xplenty or contact our support team today to find out more.