We live in a world where data collection has become one of the most important commodities available to countries, companies, and individuals. In 2017, The Economist called data a more valuable resource than oil, and as technology has evolved, specifically with the rise of artificial intelligence (AI), and tools like vector databases data has become more valuable than ever.
As we noted in an article on Data Curation, data’s importance in AI is similar to the role of blood in the human body; it is the fuel that empowers AI models to learn, grow, and adapt to make decisions. In response to this growing amount of data, a Cognitive Systems Research paper outlines how vector database management systems have emerged as an important component in modern data management, “driven by the growing importance for the need to computationally describe rich data such as texts, images, and video in various domains such as recommender systems, similarity search, and chatbots.” As more institutes invest in data collection and AI, these vector databases are becoming more and more crucial.
Vector Databases Explained
Vector databases are the new frontier of data collection, storage, and retrieval. To understand what makes a vector database different from standard databases, we have to examine what a vector is. In data science, a vector is an ordered list or sequence of numbers representing any data type, including unstructured data such as text, image, audio, and video. In a traditional database, you would only be able to search for such data using a limited number of parameters, such as tags, labels, metadata, and file names.
A vector uses the principle of embeddings to create many different data points from which to search. A guide to MongoDB’s vector databases uses an example of a large collection of cat photos to explain the point. They outline how each image is a piece of unstructured data, and you can represent components of each image as a vector by extracting features, such as the average color, the color histogram, the texture histogram, and the presence or absence of ears, whiskers, and a tail.
Each vector is converted into a sequence of numbers that can be used in a search. So, if you are looking for a specific cat photograph in a large collection of photos but don’t know the exact file name or location, you could enter data points such as two cats, the night sky, and the prominent color, and the vector database would use the vectors to find the closest matches.
Why Are Vector Databases Becoming Crucial?
They Can Search Through Massive Datasets
With data now more important than ever, there has been a heavy investment globally. Venture Beat reports that the total spend on databases and database management solutions doubled from $38.6 billion in 2017 to $80 billion in 2021 and that “databases have only further entrenched their position as one of the most rapidly growing software categories.” However, the article also reports an issue with this increasing amount of data: 80% of data stored globally has not been formatted, tagged, or structured in a way that allows it to be rapidly searched or recalled.
Vector databases have become increasingly crucial because they allow users to easily search embeddings within these massive datasets by using any data point the user deems relevant.
They Can Enhance Generative AI
With the increasing employment of software like ChatGPT, Gemini, and LlaMA, the vector database is one of the crucial components that can enhance this generative AI. These language learning models (LLMs) use vast amounts of unstructured text data, such as documents, to recognize, translate, predict, and generate text.
Aside from storing and organizing vast datasets, vector databases can handle real-time syncing to ensure the most up-to-date data is always available for retrieval. This allows generative AI applications that rely on up-to-date data to generate relevant and timely content to provide immediate answers to queries. As a result, many vector databases have been designed to integrate with AI and machine learning platforms. With more companies investing in AI, vector databases will become even more crucial to their operations.
They Are Designed For Rapid Scaling
As mentioned above, vector datasets are designed to store large datasets, and one important function is their ability to scale easily. Users can add millions, even billions, of vectors to the database as the amount of data they can collect increases. Most vector databases are designed to scale horizontally, meaning they can easily allocate the data and workload across multiple servers or nodes.
Through progressively adding nodes to the database, horizontal scalability opens up the possibility of virtually limitless expansion compared to vertical scaling, where there is an upper threshold to how much a single server can scale before it hits hardware constraints. This makes them ideal for companies and start-ups looking to expand rapidly.
