The database architecture called shared-disk, which eliminates the need to partition data, is ideal for cloud databases. Shared-disk databases allow clusters of low-cost servers to use a single collection of data, typically served up by a Storage Area Network (SAN) or Network Attached Storage (NAS). All of the data is available to all of the servers, there is no partitioning of the data.
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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