Vector Embeddings: How They Work, Where to Store Them, and Best Practices

Key Takeaways Vector embeddings convert unstructured data into numeric representations that power semantic search, recommendations, and multimodal analytics beyond keywords. Embedding success isn’t just about the model - it also depends on a data platform that can meet requirements for scale, low latency, security, and governance, including vector indexing/ANN search, access controls, encryption, and monitoring.