Your organization’s databases store all of the enterprise data that you need for your software applications, systems, and IT environments, helping you make smarter data-driven business decisions. But not all databases are created equal: the design of a database schema can have a profound impact on how efficiently the database runs and how quickly you can retrieve information.
Designing a database schema is a task that’s easier said than done—but by observing a few tips, principles, and best practices, you’ll be much better positioned for success. 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.
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Table of Contents
- What is a Database Schema?
- 6 Types of Database Schemas
- What is Database Schema Design?
- Why is Database Schema Design Important?
- How to Design a Database Schema
- Best Practices for Database Schema Design
- How Xplenty Can Help With Your Database Schema Design
What is a Database Schema?
Simply put, a database schema is a formal description of the structure or organization of a particular database. The term “database schema” is most commonly used in reference to relational databases, i.e. databases that organize information in tables and use the SQL query language. Non-relational (i.e. “NoSQL”) databases come in several different formats and are not generally considered to have a “schema” in the same way that relational databases do (although they do have an underlying structure).
Related Reading:SQL vs. NoSQL: 5 Critical Differences
There are two fundamental components of any database schema:
- Physical database schema: The physical database schema describes how data will be stored physically on a storage system and the form of storage used (files, key-value pairs, indices, etc.).
- Logical database schema: The logical database schema describes the logical constraints applied to 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 that are 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.
6 Types of Database Schemas
What are your options when it comes to the various types of database schemas? In this section, we’ll give a brief overview of some of the most common database schema types.
- Flat model: A “flat model” database schema organizes data in a single, two-dimensional array—think of a Microsoft Excel spreadsheet or a CSV file. This schema is best for simple tables and databases without complex relations between different entities.
- Hierarchical model: Database schemas in a hierarchical model have a “tree-like” structure, with child nodes branching out from a root data node. This schema is ideal for storing nested data—for example, family trees or biological taxonomies.
- Network model: The network model, like the hierarchical model, treats data as nodes connected to each other; however, it allows for more complex connections, such as many-to-many relationships and cycles. This schema can model the movement of goods and materials between locations, or the workflow required to accomplish a particular task.
- Relational model: As discussed above, this model organizes data in a series of tables, rows, and columns, with relationships between different entities. We’ll mainly be working with the relational model in the remainder of this article.
- Star schema: The star schema is an evolution of the relational model that organizes data into “facts” and “dimensions.” Fact data is numerical (e.g. the number of sales of a product), while dimensional data is descriptive (e.g. the product’s price, color, weight, etc.).
- Snowflake schema: The snowflake schema is a further abstraction on top of the star schema. Fact tables point to dimensional tables, which can also have their own dimensional tables, expanding the descriptiveness possible within the database. (As you might have guessed, the “snowflake” schema is named after the intricate patterns of a snowflake, in which smaller structures radiate off of the central arms.)
Related Reading:6 Database Schema Designs and How to Use Them
What is Database Schema Design?
Database schema design refers to the practices and strategies for constructing a database schema.
You can think of database schema design as a “blueprint” for how to store massive amounts of information in a database. The schema is an abstract structure or outline that represents the logical view of the database as a whole. By defining categories of data and relationships between those categories, database schema design makes data much easier to retrieve, consume, manipulate, and interpret.
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. Designers create database schemas to give other database users, such as programmers and analysts, a logical understanding of the data.
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Why is Database Schema Design Important?
Databases that are inefficiently organized suck up tons of energy and resources, tend to be confusing, and are hard to maintain and administer. That’s where database schema design comes into play.
Without a clean, efficient, consistent database schema, you’ll struggle to make the best use of your enterprise data. For example, the same data might be duplicated in multiple locations—or even worse, might be inconsistent between these locations.
Relational database systems heavily depend on having a solid database schema in place. The goals of good database schema design include:
- Reducing or eliminating data redundancy.
- Preventing data inconsistencies and inaccuracies.
- Ensuring the correctness and integrity of your data.
- Facilitating rapid data lookup, retrieval, and analysis.
- Keeping sensitive and confidential data secure, yet accessible to those who need it.
How to Design a Database Schema
Database schemas outline the architecture of a database, and helping to ensure database fundamentals such as the following:
- Data has consistent formatting
- All record entries have a unique primary key
- Important data is not omitted
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. For example, even though the leading database systems have slightly different definitions of what schemas are, MySQL, Oracle Database, and Microsoft SQL Server each support the CREATE SCHEMA statement.
For example, suppose that you want to create a database to hold information for your organization’s accounting department. A specific schema for this database might outline the structure of two simple tables:
Fields: ID, Full Name, Email, Date of Birth, Department
Title: Overtime Pay
Fields: ID, Full Name, Time Period, Hours Billed
This single schema contains valuable information such as:
- The title of each table
- The fields that each table contains
- The relations between tables (e.g. linking an employee’s overtime pay to their identity via their ID number)
- Any additional relevant information
Developers and database administrators can then convert these schema tables into SQL code.
Best Practices for Database 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.
- Naming conventions: Define and use appropriate naming conventions to make your database schema designs 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.
- Try not to use reserved words in table names, column names, fields, etc., which will likely 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 nouns, not plural nouns, 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 verbiage for table names (i.e. use Department instead of DepartmentList, TableDepartments, etc.)
- Security: Data security starts with a good database schema design. Use encryption for sensitive data such as personally identifiable information (PII) and passwords. Don’t give administrator roles to each user; instead, request user authentication for database access.
- Documentation: Database schemas are useful long after they’ve been created, and will be viewed by many other people, which makes good documentation essential. Document your database schema design with explicit instructions, and write comment lines for scripts, triggers, etc.
- Normalization: Briefly, normalization ensures that independent entities and relationships are not grouped together in the same table, reducing redundancy and improving integrity. Use normalization as necessary to optimize the database’s performance. Both over-normalization and under-normalization can result in worse performance.
- Expertise: 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.
How Xplenty Can Help With Your Database Schema Design
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Database schema design is an in-depth, technically complex field, and this guide to database schema design has just scratched the surface of what you need to know. Hopefully, these guidelines and best practices will help get you started on the right path when designing database schemas.
Of course, designing your database schema is just the first step in good data management. Well-designed schemas ensure that you can efficiently retrieve and analyze your data—but to actually perform this retrieval and analysis, you’ll need a good ETL tool like Xplenty.
Xplenty is a powerful, feature-rich ETL and data integration platform for building data pipelines from your databases and other sources to a centralized data warehouse in the cloud. With more than 100 pre-built connectors and integrations, and a user-friendly drag-and-drop interface, it’s never been easier to integrate your enterprise data.
Are you in the market for a cutting-edge ETL tool? Give Xplenty a try. Get in touch with our team of data experts today for a chat about your business needs and objectives, or to start your 14-day trial of the Xplenty platform.