Max iterations (100) reached for batch Resolution, please set 'spark.sql.analyzer.maxIterations' to

Hello PraveenVeli 

In Spark SQL’s context, “iterations” refer to the number of passes the query analyzer makes through the logical query plan to resolve references, infer types, and apply optimizations

Why This Applies to Your Fabric Scenario
1. Workspace Boundary Resolution
3. Fabric treats Lakehouses in different workspaces as separate catalogs, forcing Spark to:
• Verify table existence in both environments
• Reconcile schemas across workspaces
• Handle potential credential handoffs

Even for 1k rows comparison 

-- Implicitly creates nested plans for:
1) Data fetch from Lakehouse A
2) Data fetch from Lakehouse B
3) Join operation
4) Delta transaction log checks
5) Insert operation

Try

spark.conf.set("spark.sql.analyzer.maxIterations", "200")

And do df.explain(mode="extended")

Look for Cartesian products or complex subquery patterns

Try

OPTIMIZE delta_table ZORDER BY primary_key;

please give a try and let me know if this works