Vector Database for Real-Time Analytics and Vector Search

A vector database is a database designed to store and query high-dimensional vectors (embeddings). Embeddings represent the meaning of text, images, audio, products, or events as arrays of numbers. Instead of exact matches, a vector database enables similarity search: "find items most like this one".

Most teams pair vector similarity search with structured filters and metadata (tenant, time range, product category, location). In practice, vector search is rarely a standalone workload. It’s usually combined with:

• Filtering and ranking

• Joins with business data

• Aggregations and monitoring

• Continuous ingestion and freshness requirements

A vector database becomes valuable when you need to retrieve relevant items by meaning, not by keywords or IDs. Common triggers include:

• RAG and AI assistants: retrieve context chunks or documents for LLM prompts

• Semantic search: search across product catalogs, knowledge bases, tickets, or logs

• Recommendations: "people also viewed", "similar items", personalized ranking

• Matching and deduplication: detect near-duplicates, entity resolution

• Anomaly and pattern detection: compare behavior vectors over time

• Hybrid search: combine vector similarity with filters, scoring, and text search

Traditional databases are excellent for structured queries and transactions, but they are not optimized for similarity search over embeddings.

Vector databases typically add:

• Efficient indexing for nearest-neighbor search

• Similarity metrics (cosine similarity, dot product, L2 distance)

• Hybrid retrieval patterns (vector + metadata constraints)

However, many vector databases still rely on other systems for:

• High-ingestion time series and event data

• SQL analytics (aggregations, joins, grouping)

• Real-time operational dashboards

• Complex filtering at scale

That’s why teams often end up with multiple databases and pipelines.

A vector store is typically a system designed to store embeddings and perform fast similarity search. Many vector stores focus narrowly on nearest-neighbor retrieval and are optimized for AI workflows like semantic search or retrieval-augmented generation.

A vector database goes beyond similarity search. In addition to storing vectors, it manages metadata, filtering, persistence, and query execution across large datasets.

Vector databases are designed to operate as part of a broader data platform, supporting production workloads that require scalability, reliability, and integration with other data access patterns.

In practice, the distinction matters when vector search is combined with structured filters, real-time ingestion, analytics, or operational constraints. As AI systems mature, teams often need more than a standalone vector store to support end-to-end applications.

CrateDB is a distributed SQL database built for real-time analytics on fast-changing data. When you add vector search into that same platform, you can store embeddings alongside the data you use to filter, enrich, and analyze results.

What this enables:

• One data layer for AI and analytics: Store embeddings, metadata, events, and aggregates together and query them with SQL.

• Freshness for AI applications: Ingest streaming data continuously and search it immediately, without batch pipelines.

• Hybrid retrieval patterns: Combine similarity search with structured filters (tenant, time, category, region) and analytics (aggregations, rankings).

Operational simplicity: Reduce the number of systems you need to run and keep in sync.

Instead of: app -> vector DB + data warehouse + stream pipeline + feature store

You move toward: app -> CrateDB (vectors + operational data + analytics)

• RAG on real-time data: Retrieve relevant documents, logs, incidents, or knowledge base articles for an LLM, filtered by tenant, permissions, language, and time windows.

• Semantic search with filters: Similarity search across content or products, plus constraints like availability, region, price band, and recency.

• Recommendations and ranking: Find similar products or users via embeddings, then combine with behavior signals and business rules using SQL.

• Monitoring and anomaly workflows: Store embeddings derived from sensor data or event sequences, then detect similarity to known patterns and analyze trends over time.

Use this as an evaluation checklist:

• Hybrid search support: vectors + structured filters without hacks

• Ingestion throughput: can it keep up with streaming updates?

• Query expressiveness: can you join, aggregate, and rank results?

• Operational scaling: distributed architecture, fault tolerance

• Latency predictability: stable performance as data grows

• Data modeling flexibility: handle structured + semi-structured metadata

• Cost and complexity: number of systems required for the full workflow