Lakehouse Patterns for Retrieval and Semantic Search

If you already run a lakehouse, you are closer to semantic search readiness than you think.

The main challenge is not standing up a vector store. It is creating reliable data flows from curated entities into retrieval indexes.

A practical pattern

1. curate trusted gold entities
2. generate retrieval documents/chunks
3. enrich with metadata tags
4. embed and index
5. monitor freshness and drift

flowchart LR
    A[Gold Layer] --> B[Document Build]
    B --> C[Metadata Enrichment]
    C --> D[Embedding]
    D --> E[Vector Index]

Design principles

• keep source-to-index lineage
• isolate indexing failures from core BI workloads
• enforce freshness SLA for index updates
• version embeddings and chunk strategy

Final take

Semantic search quality depends on data architecture discipline.

Teams that already run clean bronze/silver/gold layers can move faster by extending existing patterns rather than creating a separate AI data stack from scratch.