What are vector databases?

Summary

• Vector databases store high-dimensional embeddings of unstructured data and use approximate nearest neighbor algorithms like HNSW and IVF to enable fast similarity search.
• Common use cases include semantic search, retrieval-augmented generation (RAG), recommendation systems, image and audio matching, and anomaly detection.
• The Databricks Platform eliminates stack fragmentation by unifying vector search, operational data, and AI workloads under one governed architecture with Unity Catalog and Lakebase.

What are vector databases?

Unstructured data, text, images, audio, video, now makes up an estimated 80% of all enterprise data, according to IDC. Traditional databases store rows and columns, but they struggle to capture meaning and relationships inside unstructured content. Vector databases store data as high-dimensional numerical representations called embeddings, enabling similarity search rather than exact-match lookups.
Teams building AI applications need to understand how vector databases work and where they fit in a modern data architecture.

How vector databases work

A machine learning model transforms unstructured data into vector embeddings. These embeddings are stored in the database alongside relevant metadata. The database groups vectors that are close together so queries can find similar items quickly.
The core workflow follows four steps:

1. Embed: A trained model converts raw data into fixed-length numerical vectors.
2. Index: The database organizes vectors using specialized index structures such as HNSW, IVF, or product quantization.
3. Query: A user's input is converted into a vector, and the database returns the closest matches by similarity.
4. Retrieve: Results are ranked and returned, often with metadata filters applied.

How vector similarity search algorithms work

Different indexing strategies trade off speed, accuracy, and memory usage. Two of the most common approaches:

Algorithm	How it works	Best for
HNSW (Hierarchical Navigable Small World)	Builds a multi-layer graph of neighbors; navigates from coarse to fine layers during search	Low-latency, high-recall queries on moderate-scale datasets
IVF (Inverted File Index)	Partitions vectors into clusters; searches only a subset of clusters per query	Large-scale datasets where memory efficiency matters

Many production systems combine techniques, for example, IVF with product quantization, to balance precision and throughput.

Common use cases for vector databases

Vector databases power a broad range of AI and data applications:

• Semantic search: Find documents by meaning, not just keywords.
• Retrieval-augmented generation (RAG): Supply relevant context to large language models so they generate grounded answers. Learn more about building and deploying RAG systems on Databricks.
• Recommendation systems: Surface similar products, content, or users based on behavioral or content embeddings.
• Image and audio similarity: Match visual or acoustic patterns across large media libraries.
• Anomaly detection: Identify outliers by measuring distance from normal patterns.

Vector databases vs. traditional relational databases

Relational databases represent data in rows and columns. They excel at structured queries, joins, and transactions. Vector databases represent data as points in high-dimensional space, clustered by similarity.

• Query model: Exact match and SQL filters vs. approximate nearest neighbor search.
• Data types: Structured records vs. dense numerical embeddings from unstructured content.
• Optimization target: Transaction throughput vs. search recall and latency.

Extensions like pgvector add vector capabilities to PostgreSQL. These work for smaller workloads but do not provide unified governance across a full data and AI stack.

Best practices for choosing and optimizing a vector database

When evaluating options, consider these vendor-neutral criteria:

• Scalability: Can it handle billions of vectors without degrading query latency?
• Index flexibility: Does it support multiple algorithms for different workloads?
• Metadata filtering: Can you combine similarity search with attribute-based filters?
• Governance and access control: How are permissions, lineage, and audit trails managed? Explore how data governance solves this challenge at scale.
• Integration: Does it connect to your embedding models, orchestration tools, and analytics layer?

For optimization at scale, batch index builds, tune parameters like IVF cluster counts or HNSW neighbor counts, and monitor recall versus latency trade-offs in production.

How the Databricks Platform consolidates vector stores into one governed architecture

Adding a standalone vector store introduces another component into an already fragmented stack. Teams end up stitching together operational databases, feature stores, vector stores, model endpoints, and orchestration layers, each with its own governance model.
The Databricks Platform addresses this with a unified approach. Lakebase provides a unified operational foundation: OLTP data, application state, and operational logic live directly on the same storage layer as enterprise data and AI.

• One catalog for all data. Unity Catalog manages Delta Lake, Apache Iceberg™, and Parquet with a single set of permissions, lineage, and business definitions.
• AI that understands your data. The platform learns the meaning, context, and usage of your data, keeping metrics consistent and powering AI agents with trusted answers.
• No stack fragmentation. AI Search capabilities sit alongside analytics, BI, and AI workloads, all governed in one place.

FAQs

How do vector databases store and retrieve high-dimensional data?

They store fixed-length numerical vectors produced by embedding models, then use approximate nearest neighbor algorithms to return the most similar results at query time.

What are the most common use cases for vector databases?

Semantic search, RAG for large language models, recommendation systems, image and audio similarity matching, and anomaly detection.

How do vector similarity search algorithms like HNSW and IVF work?

HNSW builds a multi-layer graph navigated from coarse to fine layers. IVF partitions vectors into clusters and searches only relevant subsets per query.

Why are vector databases important for building AI and machine learning applications?

They let AI systems retrieve contextually relevant unstructured data at low latency, which is critical for grounding model outputs in real-world information.

How are vector embeddings created and used in a vector database?

A trained machine learning model converts raw data into fixed-length numerical vectors. The database indexes these vectors and retrieves the nearest matches during similarity search.

What is the difference between a vector database and a traditional relational database?

Relational databases use rows, columns, and exact-match queries. Vector databases cluster high-dimensional embeddings by similarity for semantic queries over unstructured data.

How do vector databases support retrieval-augmented generation (RAG) for large language models?

Documents are chunked and encoded into embeddings. At query time, the most relevant chunks are retrieved by similarity and passed to the LLM to generate grounded answers.

What are the key features to look for when choosing a vector database?

Scalability, index flexibility, metadata filtering, governance and access control, and integration with embedding models and analytics tools.

How do you index and optimize queries in a vector database for large-scale datasets?

Use batch index builds, select the right algorithm for your latency and recall needs, tune parameters like cluster counts, and apply metadata pre-filters to narrow the search space.

Can existing databases like PostgreSQL be used as vector databases with extensions?

Yes. The pgvector extension adds a VECTOR data type and similarity search operators. However, extensions serve single-database workloads and do not unify governance across a broader data and AI stack.
Explore how Lakebase unifies vector search, operational data, and AI workloads in a single governed architecture.