concept#Data#Platform#Software Engineering

Vector Database

A vector database stores and queries high‑dimensional vectors for semantic search and similarity retrieval. It optimizes index structures and ANN algorithms to enable fast, scalable embedding queries.

Vector databases are specialized stores for dense vector representations (embeddings) that provide indices, approximate nearest neighbor algorithms and distance metrics for fast semantic and neighborhood search.

Maturity

Established

Cognitive loadMedium

360° overview

Vector Database

Six perspectives place the building block in context. The numbers show where each perspective continues in the reading path.

The building block at a glance

Vector Database

360°

Classification

2

Complexity: Medium
Impact area: Technical

Technical context

2

Integrations

Feature store as embedding source

+2

Principles & goals

1

Separate vector storage from metadata layers

Value stream stage: Build

Use cases & scenarios

4

Use cases: 3
Scenarios: 3

Compromises

3

Risks: 3
Best practices: 3

I/O & resources

4

Inputs: 3
Outputs: 3