Vector Database vs Traditional Database
Below are the some key differences between Vector and Traditional Database:
Feature |
Vector Database |
Traditional Database |
---|---|---|
Data |
Structured and unstructured data |
Vector data |
Search |
Predefined criteria for search |
Based on the context or vector distance |
Data Processing |
Optimized for analytical queries and aggregations. |
Suitable for transactional operations and ad-hoc queries. |
Storage Efficiency |
Optimized for storing and querying large volumes of data efficiently. |
May have less optimized storage for analytical workloads. |
Use Cases |
Semantic search, Ideal for time-series data, IoT applications, and real-time analytics. |
Commonly used for traditional business applications, OLTP, and OLAP workloads. |
Examples |
Pinecone, chroma, Milvus |
MySQL, PostgreSQL, Oracle, SQL Server. |
What is a Vector Database?
In the field of data handling, the standard database has been an icon for storing and retrieving data. Nevertheless, despite the fact that the amount of data and complexity are constantly increasing, there are new technologies appearing that break the previous limitations of conventional database systems.
Of the many innovations that have come with the Vector Database is a strong tool that can manage high dimensional data in a more efficient manner. This article looks at what a Vector Database is, how it functions, and the potential it holds for the evolution of data storage.