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Title: How Do Vector Databases Work & Why are Businesses Adopting Them?

The database market is experiencing explosive growth, fueled by the ever-increasing data needs of users worldwide. The Global Database Management System market is projected by HTF Market Intelligence to see a 10% CAGR between 2023 and 2028. But within this vast landscape, a new type of database is gaining rapid adoption – vector databases.Vector databases offer a unique approach for complex, high-dimensional data types like images, text embeddings, and sensor readings. This article explores the inner workings of vector databases and delves into why businesses across various tech sectors embrace them.Making Vectors WorkTraditional relational databases organize data in rows and columns. Vector databases, on the other hand, store information as vectors. Think of a vector as a multi-dimensional data module in a designated space. A guide to vector databases on MongoDB explains a vector as having dimensions like magnitude and direction that represent specific features of the data. For instance, if it's stored in a two-dimensional vector space, it can have vertical and horizontal direction components. This approach allows for efficient storage and retrieval of complex data, particularly when dealing with relationships between pieces of information.Vector databases excel at similarity search, a technique for finding data points that are close together in the vector space. This translates to real-world applications such as a recommendation engine. Spotify, for example, uses Apache Spark, a distributed computing framework with vector processing capabilities, to personalize music recommendations. By analyzing user listening habits and representing songs as vectors, they can identify similar music and curate personalized playlists for its users.Another application is in f...

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