What is Data lakehouse?
A lakehouse stores raw and curated data together, enabling BI, data science, and AI on one platform. Think of it as a single, unified place for all your data, combining the vast, flexible storage of a 'data lake' with the trustworthy, organized structure of a 'data warehouse' (designed for business intelligence).
How does it work?
Ingest data, store it in open formats, build curated tables, and expose a semantic layer for analytics and AI. Govern access and lineage so teams can trust what they use.
When does it matter? (Examples)
- AI assistants need reliable, governed context.
- Multiple tools must read from the same, up‑to‑date source.
- You want to retire overlapping data pipelines and costs.
Benefits
- Unifies data for AI/BI
- Reduces duplication
- Improves governance
Risks
- Data sprawl without modeling discipline
- Performance issues at scale
- Underused governance features
Antire and Data lakehouse
We design lakehouse patterns and semantic layers so RAG and analytics read trusted data, backed by access control and lineage.
Services
Data platforms and applied AI
Related words
Microsoft Fabric, Delta/Parquet, Semantic layer, RAG, ELT, Data governance