Relational databases are optimized to index specific columns for easy traversal, while graph databases focus on nodes and edges for efficient graph traversals. Vector databases deal with high-dimensional sets of numbers (vectors) and are specialized in finding similarities between vectors in a high-dimensional space. They use specific algorithms to index and query data quickly, retrieve similar vectors, and update the vector space efficiently.
Daniel & Chris explore the advantages of vector databases with Roie Schwaber-Cohen of Pinecone. Roie starts with a very lucid explanation of why you need a vector database in your machine learning pipeline, and then goes on to discuss Pinecone’s vector database, designed to facilitate efficient storage, retrieval, and management of vector data.
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