hi @Dharinip 

Cleaning up the shared log shows "Before" and "After" fingerprints. Hopefully can give us more info in how to proceed

• Before

[
  {
    "id": 12
  },
  {
    "qualifier": []
  },
  {
    "class": "GreaterThan",
    "num-children": 2,
    "left": 0,
    "right": 1
  },
  {
    "class": "Literal",
    "num-children": 0,
    "value": "2025-07-08 04:32:01.821544",
    "dataType": "timestamp"
  },
  {
    "class": "Cast",
    "num-children": 1,
    "child": 0,
    "dataType": "timestamp",
    "timeZoneId": "Etc/UTC",
    "evalMode": null
  },
  {
    "class": "AttributeReference",
    "num-children": 0,
    "name": "none",
    "dataType": "string",
    "nullable": true,
    "metadata": {},
    "exprId": {
      "product-class": "ExprId",
      "id": 16
    },
    "qualifier": []
  },
  {
    "class": "GreaterThan",
    "num-children": 2,
    "left": 0,
    "right": 1
  },
  {
    "class": "Cast",
    "num-children": ...
  }
]

• After:

[
  {
    "id": 12
  },
  {
    "qualifier": []
  },
  {
    "class": "LessThan",
    "num-children": 2,
    "left": 0,
    "right": 1
  },
  {
    "class": "Literal",
    "num-children": 0,
    "value": "2025-07-08 05:18:31.155472",
    "dataType": "timestamp"
  },
  {
    "class": "Cast",
    "num-children": 1,
    "child": 0,
    "dataType": "timestamp",
    "timeZoneId": "Etc/UTC",
    "evalMode": null
  },
  {
    "class": "AttributeReference",
    "num-children": 0,
    "name": "none",
    "dataType": "string",
    "nullable": true,
    "metadata": {},
    "exprId": {
      "product-class": "ExprId",
      "id": 17
    },
    "qualifier": []
  },
  {
    "class": "GreaterThan",
    "num-children": 2,
    "left": 0,
    "right": 1
  },
  {
    "class": "Literal",
    "num-children": ...
  }
]

After running a diff: 

Brainstorming, may I suggest:

1. Isolate Problematic Aggregations and Transformations
   • Start simple: Begin with a minimal version of your materialized view (MV) that includes only a few basic aggregations or transformations.
   • Incrementally add complexity: Gradually introduce additional aggregations or transformations, testing after each change.
   • Identify the trigger: Continue this process until the error reappears. This helps pinpoint the specific operation or transformation causing the issue.
2. Check for Non-Deterministic User-Defined Functions (UDFs)
   • Review all UDFs used: Examine any UDFs used in your MV for non-deterministic behavior (e.g., functions that use randomness, current timestamps, or external state).
   • Replace or refactor: If you find non-deterministic UDFs, consider replacing them with deterministic alternatives or refactoring your logic to avoid them, as non-determinism can disrupt incremental processing and caching.
3. Validate Input Data Consistency
   • Inspect source data: Ensure that the data being aggregated or transformed is stable and consistent between runs.
   • Check for upstream changes: Verify that referenced tables, columns, or views have not changed unexpectedly, as such changes can alter the query plan and trigger errors.

Thank you, and I hope this helps you a bit to find the issue.