[Comprehensive Guide] What’s the Difference Between Relational Databases and Vector Databases? – Essential Knowledge for Modern Data Utilization
Introduction: Why You Should Know the Difference
With the growing prevalence of AI and machine learning, the term “vector database” is increasingly appearing in discussions. Meanwhile, “relational databases,” which have long supported the backbone of information systems, still play a central role in many businesses and organizations.
In this article, we’ll thoroughly explain the differences between relational databases and vector databases, and provide concrete examples to help you decide which one to use in different scenarios.
This information is extremely valuable for system engineers working with databases, AI developers, IT department personnel, and decision-makers in management.
What Is a Relational Database (RDB)?
A Relational Database is a system that manages information in table format consisting of “rows” and “columns.” First introduced in the 1970s, it’s still widely used in many business systems today.
Key Features:
- Data is stored in tables
- Each row is called a “record,” and each column is a “field” or “column”
- Relationships between data are managed using “keys” (primary key, foreign key)
- Data is manipulated using SQL (Structured Query Language)
Common Use Cases:
- Customer management systems
- Accounting software
- Inventory management
- Hospital medical records
Example Structure:
| Customer ID | Name | Email Address | Purchase Count |
|---|---|---|---|
| 001 | Taro Yamada | taro@example.com | 12 |
| 002 | Hanako Sato | hanako@example.com | 5 |
As you can see, information is clearly structured, making it easy to search, aggregate, and update—this is the key advantage of RDBs.
What Is a Vector Database?
A Vector Database is a database that stores information as numerical vectors and enables high-speed similarity searches. It is especially useful in fields such as AI, natural language processing, image recognition, and recommendation systems.
Key Features:
- Data is stored as high-dimensional vectors
- Searches are based on “similarity” (e.g., cosine similarity, Euclidean distance)
- Excellent for searching unstructured data like text, images, and audio
- Operations are usually non-SQL-based (handled via APIs or libraries)
Common Use Cases:
- Chatbot question-answering engines
- Product recommendation systems
- Similar image search
- Voice command recognition systems
Example Structure:
For example, a product description might be vectorized as follows:
“Lightweight and durable running shoes” → [0.21, -0.54, 0.13, …, 0.87]
By searching for vectors similar to this one, the system can quickly extract “products with similar characteristics.”
Feature and Use Case Comparison
| Item | Relational Database (RDB) | Vector Database (Vector DB) |
|---|---|---|
| Data Structure | Table format (rows & columns) | Numerical vectors (high-dimensional space) |
| Search Method | Key search, conditional search (SQL) | Similarity search (e.g., cosine similarity) |
| Best Suited For | Structured data (numbers, dates, text) | Unstructured data (text, images, audio) |
| Main Use Cases | Business management systems | AI-driven search, similar data retrieval |
| Search Accuracy & Speed | Retrieves data that exactly matches conditions | Quickly extracts similar data with high precision |
Synergy Through Combined Use
Recently, hybrid search, which combines relational and vector databases, is gaining attention.
For example:
- Basic customer information (RDB)
- Content of customer inquiry emails (Vector DB)
By combining the two, it becomes possible to flexibly present “similar inquiries linked to customer information from the RDB.”
Real-World Example:
A company building an FAQ system vectorizes customer questions and searches for similar questions using a vector DB. Then, detailed answers and contact person info are retrieved from the RDB, forming a robust system.
Accessibility Considerations
This article was written to be accessible for those who prefer non-visual information formats, are unfamiliar with technical terms, or are non-native Japanese speakers.
- Technical terms are explained with supplementary descriptions
- Tables and bullet points are used to improve clarity
- A balance of kanji, hiragana, and katakana ensures readability
- Short, clear sentences are used instead of long paragraphs
These considerations help make the content understandable regardless of one’s technical background or physical conditions.
Summary: Which One Should You Use?
In conclusion, choosing the right tool based on each database’s characteristics is crucial.
- Use relational databases for centralized business data management
- Use vector databases for similarity-based search features
- For advanced systems, a combination of both is ideal
In the modern data era, combining “data structuring” and “semantic similarity” enables deeper analysis and better services.
Understanding the strengths and applications of both types of databases is the first step toward leveraging data as a strategic asset.
We hope this guide helps you make better use of your data.