LHO Core Pillars

Lakehouse Optimizer (LHO) provides organizations with visibility, control, and actionable insights that help improve Databricks cost efficiency, performance, and operational governance.

LHO is structured around four core pillars:

1. Cost Management & Control
   Visibility into spend, forecast, budgets, commits, and efficiency trends. Flexible reporting lenses, including by provider (Databricks vs. cloud services), by compute feature, by environment, or by cost‑allocation metadata such as tags. Cost Control incidents act as guardrails to surface unexpected changes or risks.

2. Optimization Recommendations
   LHO analyzes workload telemetry to surface actionable insights that improve performance and reduce unnecessary spend. The goal of this pillar is to guide teams toward data‑driven decisions that enhance efficiency while maintaining reliability.

3. UC Migration Support
   LHO provides visibility into legacy usage, active references to older systems, and progress toward modernized access paths. This helps organizations move toward unified, governed, and simplified data architectures at an informed and manageable pace.

4. Vendor Consolidation
   Exposure of external and legacy systems endpoints detected during workload execution. By surfacing these references, LHO enables teams to identify non‑standard access patterns, address fragmentation, and reduce dependencies on systems outside the intended architecture.

Collectively, these pillars create a unified optimization framework for organizations to operate Databricks more efficiently, reliably, and confidently. They serve as the foundation for all additional capabilities in LHO, including KPI tracking and telemetry.