Introduction :

Challenges :

Handling Large Scripts with Token Limitations

1. Script Chunking : Large ETL scripts exceeding AI model token limits require chunking into manageable chunks. This involves identifying logical separation points while preserving workflow integrity. The process demands careful handling of interconnected transformations, with particular attention to maintaining variable scope and temporary table references across segments.

2. Dependencies and Context : Breaking scripts into chunks can make it hard to track how different parts connect and depend on each other. It's important to maintain clear links between related jobs and ensure data flows correctly between the separate pieces

3. Merging and Execution Flow : The merging phase of chunked ETL scripts demands meticulous attention to execution order and interdependencies. When reassembling the AI-translated chunks, organizations must establish the correct sequence of operations that mirrors the original workflow. This includes understanding and maintaining the proper order of table creations, data transformations, and dependent procedures. The process becomes particularly complex when dealing with temporary tables and intermediate results that flow between different chunks of the script.

Validation of Generated Scripts

1. Code and logic Validation : Validating AI-generated ETL code requires a comprehensive approach that goes beyond basic syntax checking. Teams must verify that business rules are accurately translated, data transformations maintain precision, and error handling remains robust. This involves testing various scenarios including edge cases, null handling, and complex conditional logic. The validation process must also ensure that performance optimizations are maintained and that the code effectively utilizes the target platform's features while maintaining data integrity.

2. Data and Integration Testing : Comprehensive validation encompasses source-to-target data reconciliation and verification of complex transformation accuracy. Teams must conduct thorough end-to-end workflow testing to ensure seamless integration with dependent systems. This includes validating transaction boundaries and performing detailed performance benchmarking across environments. The testing process must account for various data scenarios and processing conditions to ensure reliable operation in production.

Possible Solutions :

1. Leveraging Models with Large Output Token Capacity : The introduction of models like o1 by OpenAI with significantly larger output token limits offers a breakthrough solution for ETL migration challenges. Unlike traditional models limited to smaller outputs, these advanced models can generate much longer scripts in a single response, fundamentally changing how we approach ETL migration.The expanded output capacity enables these models to translate larger ETL scripts in one go, reducing the need for complex chunking strategies. This capability significantly minimizes the challenges of maintaining context and reconstructing workflow logic that typically arise when breaking scripts into smaller pieces. With larger output limits, the models can maintain consistency throughout the translation process and better preserve the interdependencies between different components of the ETL workflow.

1. LLM as Validator and Multi Model Validation Strategy : Employing a different model for validation than the one used for migration creates a robust quality assurance framework. This approach leverages diverse perspectives and capabilities, as different models may excel at different aspects of code analysis. The validator model can perform comprehensive comparisons of source and target code semantics, while simultaneously identifying potential logic discrepancies and verifying transformation rules. This multi-model strategy helps catch issues that might be missed by a single model or traditional testing methods.

Conclusion :