Hi, the current way is not optimal. You can follow below

INSERT query ran with mostly 43 tasks, creating 43 output files. Since the Liquid clustered table has no organization (clusterBy "[]") - dates are randomly scattered across files.

• Partition table did a clean partition-level swap as all date='2026-06-02' data is isolated by partitioning
• In Liquid Clustering with Auto - Data is scattered - mostly both dates mixed across files. REPLACE USING scanned all 86 files, identified which 43 files contained date='2026-06-02' followed by rewrite. It's an expensive operation (numDeletedRows: 30M)

You can follow below to reduce file count during writes

1. Control in Configuration

SET spark.sql.shuffle.partitions = 16

INSERT INTO <target> REPLACE USING (BED)
SELECT <cols> FROM <table>

2. Control in Repartition

INSERT INTO <target> REPLACE USING (BED)
SELECT /*+ REPARTITION(16) */ <col> FROM <table>;

Partitioning is simpler and faster than liquid clustering in this case (With 2 date values and full replacements). Liquid clustering wins when you have high cardinality or multi-dimensional queries.