Is there a way to standardize/optimize the conversion so it doesn’t blow up into thousands of lines?

Yes, there are actionable methods to standardize and optimize LakeBridge conversions to prevent code from ballooning into thousands of lines, but these require planning, tool configuration, and post-conversion steps.

Best Practices for Optimization

• Pre-Migration Assessment: Use LakeBridge’s Profiler and Analyzer components to thoroughly assess code complexity before conversion. This surfaces procedures and queries that are likely to "blow up" and flags problematic patterns early, allowing for manual pre-optimization.

• Modularize Legacy Code: Refactoring complex stored procedures and long SQL scripts into smaller, modular units reduces the risk of expansive code generation. Breaking up nested joins and expressions leads to tighter, more manageable output from the transpiler.

• Configure Transpiler Options: LakeBridge is pluggable—investigate switching between BladeBridge and Morpheus, or try modifying transpiler settings for join expansion, mapping, and fallback logic. Fine-tuning converter configuration can help standardize output size and reduce repetition, especially for JOIN/SET explosion issues.

• Manual Standardization: After automated conversion, manually merge repetitive SET or SELECT blocks and optimize procedural constructs. Using Databricks notebooks for visualizing and iterative editing makes this process easier to manage and audit.

• Advanced Tools: Consider using modern, AI-powered migration engines or custom plugins that provide semantic understanding and iterative feedback. While not yet integrated into LakeBridge, some partners offer these as add-ons or separate migration workflows