Incremental vs. Global Layout Evolution.

Traditional optimization (Z-Ordering) is a "Global Sort" operation. It is rigid; you define the columns at runtime (OPTIMIZE ZORDER BY col), and the engine tries to sort the target data partitions globally. This is expensive and doesn't adapt well to new data arriving constantly. Liquid Clustering uses an incremental algorithm based on Space-Filling Curves (Hilbert Curves). It treats data layout as a continuous state management problem, not a one-off sort job.

Application: The command interface for Liquid Clustering is simplified to just OPTIMIZE table.

Metadata Awareness: The engine looks at the table definition to know how to cluster (CLUSTER BY region, user_id). You don't need to repeat the columns in the command.
Incremental Intelligence: Instead of rewriting the whole table, Liquid Clustering identifies which specific files or partitions have "drifted" from the ideal clustering layout (e.g., new unclustered files from ingestion). It rewrites only those files.
Conflict Resolution: If you change the keys (ALTER TABLE CLUSTER BY new_col), the next OPTIMIZE job simply starts clustering new data with the new key and lazily rewrites old data over time. This makes maintenance a low-touch, automated background task rather than a complex manual tuning operation.

Exam: exam1

Scalability: It uses \"File Notification\" mode (subscribing to cloud storage events like SQS/Event Grid) to avoid listing massive directories, making it performant even with millions of files.
Schema Evolution: It has advanced features to handle drifting schemas (new columns) and data quality issues (type mismatches) by using a \"rescued data\" column, ensuring pipelines don't break when data changes.