8.3 — Designing Semantic Models for Direct Lake

Direct Lake expects clean, structured, business-ready data. The model design is what separates fast dashboards from slow ones.

Best Practices:

• Use Gold tables — Direct Lake expects business‑ready data, never Bronze or Silver
• Use Star Schema — fact + dimension tables, not wide flat files
• Use Surrogate Keys — improve join performance
• Use Aggregation Tables — pre‑compute KPIs to boost performance
• Avoid Wide Tables — keep fact tables narrow
• Use Hierarchies — improve drill‑down in reports
• Use RLS / OLS — secure data access at the model level

Semantic models must be clean and structured. The cleaner the Gold layer, the faster and more reliable the BI layer.

Optimizing Delta Tables for Direct Lake

Direct Lake performance is only as good as the Delta tables underneath it. Optimization is not optional at enterprise scale.

Key Optimizations:

• Partitioning — partition fact tables by date or region to limit scan size
• Z‑Order — optimize columnar reads on frequently filtered columns
• File Compaction — avoid tiny files that slow down reads
• Vacuum — clean up old Delta files to reduce storage and improve performance
• Schema Evolution — handle column changes safely without breaking downstream models
• Merge Optimization — improve upsert performance for incremental loads

A poorly optimized Gold table means slow dashboards regardless of compute capacity. Optimization is where real performance is won.