How data lakehouses bridge the gap between lakes and warehouses

Modern organizations struggle to balance data lakes and warehouses, often operating in silos that create inefficiencies and missed analytics opportunities. Traditional methods lack the unified platform needed for AI, machine learning, and business intelligence workloads.

This white paper explores how data lakehouses bridge this gap by combining scalable storage with transactional management. It covers strategies using advanced hardware to optimize analytics:

· Three-tier architectures balancing cost and performance
· Software-defined storage for flexibility without vendor lock-in
· AI infrastructure for large language models

Build a unified platform for all your analytics needs.