Scaling Vector Databases With Novel Partitioning Methodologies
Imagine: A newly acquired dataset is being prepared for use as a vector database to retrieve information, create recommendation systems, be used for threat detection or similarity-based alert triage. During integration, however, operational challenges surface. Platform constraints prevent full-scale ingestion, prompting an arbitrary reduction in the size of the dataset. As a result, performance degrades significantly.