Modern applications rarely rely on a single data model. Relational databases remain essential for transactional consistency and structured business data. However, document, key-value, column-oriented, graph, and vector databases are now critical for workloads that require flexible schemas, horizontal scalability, low-latency access, or specialized queries. As a result, polyglot persistence — selecting the most appropriate database model for each use case — has become a standard architectural strategy rather than an exception.The rise of artificial intelligence further supports this trend. Retrieval-augmented generation (RAG), semantic search, recommendation systems, and autonomous agents often rely on embeddings and vector similarity searches to access contextual information. As a result, vector databases and multimodel NoSQL platforms are becoming integral to the modern enterprise data landscape.