SQL, NoSQL and Vectors, What’s the Difference?

Database systems have been fundamental to information technology, supporting everything from basic applications to intricate enterprise systems. They play a crucial role in organizing, storing and retrieving large volumes of data, enabling informed decision-making and strategic planning.

As technology has progressed, database technology has evolved to address the growing complexity and diversity of data management needs — starting with structured SQL databases, moving to NoSQL databases and now advancing to vector databases. Each stage marks a shift in the way data is stored, retrieved and managed. While each database type is tailored for specific applications, the common goal remains: to store, retrieve and manage data efficiently and effectively.

SQL Databases: The Foundation of Structured Data

SQL databases, also known as relational databases, were the first widely adopted database systems, emerging in the 1970s with the development of IBM‘s System R and the theoretical foundation provided by Edgar F. Codd. These databases are built on a structured schema that defines tables, rows and columns to store data. The image below shows an example of a customer table in a relational database.

Strengths of SQL Databases:

• ACID compliance: SQL databases guarantee transactions’ atomicity, consistency, isolation and durability, making them ideal for applications where data integrity is paramount.
• Complex querying: The structured nature of SQL databases allows for complex queries using SQL (Structured Query Language), which can join multiple tables and retrieve specific data.
• Mature ecosystem: With decades of development, SQL databases like MySQL, PostgreSQL and Oracle offer robust support, tools and community resources.

The NoSQL Revolution: Embracing Flexibility and Scalability

In response to the changing needs of modern applications, particularly those requiring handling large volumes of unstructured and semi-structured data such as social media posts, sensor data and web content, NoSQL databases emerged in the early 2000s. Unlike SQL databases, NoSQL databases do not require a fixed schema, allowing them to store data more flexibly. NoSQL databases come in various forms, including document databases like CouchDB, key-value stores like etcd, column-family stores like Cassandra and graph databases like Neo4j. Take a look at these types of NoSQL databases in the image below:

Strengths of NoSQL Databases:

• Horizontal scalability: NoSQL databases are designed to scale out by distributing data across multiple servers, making them ideal for handling large-scale, high-traffic applications.
• Schema flexibility: The lack of a fixed schema allows for rapid iteration and the ability to store unstructured or semi-structured data, such as JSON, XML or even multimedia files.
• High availability: Many NoSQL databases prioritize availability and partition tolerance, often sacrificing strict consistency in favor of greater uptime and fault tolerance.

Vector Databases: Powering the Next Generation of AI

We have seen that the rise of unstructured and semi-structured data led to the rise of No-SQL databases. In modern times, the need to address the complexities and nuances of gaining insights into unstructured data has led to the emergence of new types of databases called vector databases. These databases are specifically designed to store and query vector embeddings, which are mathematical representations of unstructured data like text, images and audio.

Vector databases are optimized for managing vector data, which differs from traditional databases’ structured rows and columns. Instead of storing text or numbers in a table, vector databases store dense, high-dimensional vectors generated by AI models. These vectors capture the essence of unstructured data, allowing for powerful similarity searches and data retrieval. A good example of a vector database is Milvus, which is the most popular vector database in terms of GitHub stars. Take a look at the image below that shows how a flower is represented in high-dimensional vectors.

A crucial feature of vector databases is the approximate nearest neighbor (ANN) search. ANN search enables the system to quickly find vectors most similar to a given query vector, which is essential for applications like image retrieval, recommendation systems and natural language processing.

Benefits of Vector Databases:

Vector databases offer several key advantages that make them indispensable in AI-driven applications. Let us take a look at some of these benefits:

• Scalability: Vector databases such as Milvus are designed to handle vast amounts of vector data, making them ideal for large-scale AI applications. They can scale horizontally, distributing data across multiple nodes to ensure high availability and fault tolerance.
• Efficiency in high-dimensional search: Traditional databases struggle with the complexity of high-dimensional data. Vector databases, on the other hand, are built specifically to perform efficient similarity searches on such data, enabling quick and accurate retrieval of relevant vectors.
• Integration with AI pipelines: Vector databases seamlessly integrate with machine learning models and AI pipelines, facilitating the storage, retrieval and processing of vector data. This integration is crucial for developing end-to-end AI solutions that require real-time data processing and analysis.
• Enhancing AI with context: In retrieval-augmented generation (RAG) systems, vector databases store domain-specific knowledge externally, supplying the large language model relevant context during generation. This reduces hallucinations in large language models (LLMs) and improves the accuracy of their outputs, especially in applications requiring precise, context-aware responses.

Differences Between SQL, NoSQL and Vector Databases

For a more concise comparison between SQL, NoSQL and vector databases, take a look at the table below:

Origin Article: https://thenewstack.io/sql-nosql-and-vectors-oh-my/