--- title: "Debunking 8 data layout myths: why Liquid Clustering outperforms partitioning" url: https://daily.dev/posts/debunking-8-data-layout-myths-why-liquid-clustering-outperforms-partitioning-lxazrlipq source_url: https://www.databricks.com/blog/debunking-8-data-layout-myths-why-liquid-clustering-outperforms-partitioning type: article source: "databricks" published: 2026-06-01T15:13:25.102Z updated: 2026-06-01T15:21:40.167Z tags: ["apache-spark", "databricks"] reading_time: 9 upvotes: 12 comments: 2 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Debunking 8 data layout myths: why Liquid Clustering outperforms partitioning **[databricks](https://daily.dev/sources/databricks)** · 9 min read · 12 upvotes · 2 comments ## Summary 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. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?](https://daily.dev/posts/scaling-ml-inference-on-databricks-liquid-or-partitioned-salted-or-not--j2d9kjxb6) · Towards Data Science · 7 upvotes · 0 comments --- Tags: [#apache-spark](https://daily.dev/tags/apache-spark), [#databricks](https://daily.dev/tags/databricks) [View this post on daily.dev](https://daily.dev/posts/debunking-8-data-layout-myths-why-liquid-clustering-outperforms-partitioning-lxazrlipq)