Is there any known limitation or specific rule in LakeBridge that causes such variation?

Yes, there are specific known limitations and rules in LakeBridge (Databricks’ open-source migration tool) that can cause variation—especially when converting complex SQL code or stored procedures.

Key Limitations and Rules Affecting Variation

• LakeBridge uses a static, rule-based translation method. It relies on predefined pattern-matching configurations to convert SQL and ETL code, which works well for simple, standard patterns. However, for complex scripts—particularly those with nested queries, multiple JOINs, or unusual SQL logic—the output may vary significantly even for similar input scripts.

• The transpiler may expand scripts with multiple LEFT JOINs or complex structures into long sequences of row-by-row assignments (such as repeated SET … = (SELECT …) blocks). This is a fallback to ensure correctness when the original structure is too complicated for rule-based patterning, sometimes causing the converted code to become vastly longer and harder to read.

• Minor differences in source scripts (such as alias usage, join order, or placement of expressions) can trigger different internal representations, which leads the transpiler to handle seemingly similar logic in inconsistent ways.

• LakeBridge currently offers no comprehensive value-level data validation. It reconciles data at the schema, column, and row level, but cannot guarantee perfect 1:1 parity on a field-by-field basis without external tools.

• The translation process is not iterative or AI-driven—once it attempts a conversion, there is no built-in feedback loop to optimize or correct results until full semantic or value-level equivalence is achieved