OBJECT STORAGE | FILE STORAGE | |
PERFORMANCE | Performs best for big content and high stream throughput | Performs best for smaller files |
GEOGRAPHY | Data can be stored across multiple regions | Data typically needs to be shared locally |
SCALABILITY | Scales infinitely to petabytes and beyond | Potentially scales up to millions of files, but can’t handle more |
ANALYTICS | Customizable metadata, not limited to number of tags | Limited number of set metadata tags |
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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