Implementing Vector Search in Databricks

Search has always been at the heart of analytics. Whether you’re tracking down the right transaction, filtering a customer record, or pulling a specific review, the default approach has traditionally been keyword search.

Keyword search is simple and effective when you know exactly what you’re looking for, but it quickly falls apart when the language is messy, ambiguous, or when meaning matters more than exact words. That’s where vector search changes the game. Instead of matching literal keywords, vector search relies on embeddings — high-dimensional numeric representations of text, images, or other unstructured content — that capture semantic meaning.