Debunking 8 data layout myths: why Liquid Clustering outperforms partitioning

Databricks engineers debunk 8 common myths about Hive-style partitioning versus Liquid Clustering for data layout in modern Lakehouses. Key findings include: directory-level pruning is a myth on Delta/Iceberg (file-level stats are used instead), Liquid handles low-cardinality columns with 35% faster clustering and 22% faster queries, metadata-only DELETEs run ~90% faster with Liquid, and OPTIMIZE planning time on 10 PB tables dropped from 12 hours to 23 minutes. Real-world case studies from Arctic Wolf (3.8+ PB security telemetry) and Bolt (TB-scale CDC table) show significant read/write improvements. Databricks also previews co-clustered joins (51% faster, 87% less shuffle) and an in-place partitioned-to-Liquid conversion command. The post argues Liquid Clustering is the modern default for lakehouse data layout, replacing partitioning across all scales.