Data Lakehouse

Data Lakehouse is a modern data management architecture that combines the cost-efficiency, flexibility, and scale of a data lake with the transactional integrity, governance, and query performance of a traditional data warehouse. It serves as a unified repository for both structured and unstructured analytical workloads.

How it Works

The data lakehouse architecture separates storage from compute, storing data in open file formats like Parquet on inexpensive object storage. A transactional metadata layer sits on top of these files, coordinating atomic transactions, enforcing schema rules, and managing partitioning metadata dynamically.

• Storage Layer: Utilizes scalable object stores to host files, reducing storage costs compared to proprietary warehouses.
• Compute Isolation: Enables multiple specialized engines to query the same underlying tables simultaneously without resource contention.

Semantic & Agentic Relevance

By eliminating the need to duplicate and move data between lakes and warehouses, the lakehouse provides a single source of truth for semantic models. This consolidation simplifies metadata governance, providing AI agents with reliable, up-to-date data for RAG and Text-to-SQL workflows. Dremio realizes the lakehouse vision by delivering direct, high-performance query execution on open formats like Apache Iceberg, eliminating the need for database extraction pipelines.