Modernizing 20,000 legacy data mappings with AI assisted conversion
A structured modernization approach for organizations carrying thousands of legacy ETL mappings, SQL transformations and stored procedures. AI was used to analyze existing logic, accelerate conversion and create migration documentation, while engineers retained control over validation, exception handling and production release.
A large legacy estate made manual migration slow, expensive and difficult to scale.
The organization had accumulated thousands of ETL mappings and transformation assets over time. Rebuilding them manually on a modern data platform would require engineers to understand old logic, recreate transformations, test outputs and document every migration.
What we found
- Thousands of mappings contained different transformation patterns, naming conventions and business rules.
- Some mappings were straightforward while others contained nested logic, lookups, joins and reusable components.
- Legacy documentation was incomplete or inconsistent across data domains.
- Engineers had to manually inspect source and target fields before rebuilding each mapping.
- Manual conversion created a risk of introducing subtle differences in transformation logic.
- Testing and reconciliation became a major part of the migration effort.
- Business and technical dependencies made it difficult to migrate everything in a single wave.
What the business needed
- A repeatable migration factory capable of handling thousands of mappings in controlled waves.
- AI assistance to understand legacy transformation logic and generate target code.
- A standard way to classify simple, medium and complex migration assets.
- Automated validation and reconciliation wherever possible.
- Clear handling of exceptions that required experienced engineers.
- Migration documentation generated alongside the converted assets.
- A measurable view of conversion progress, quality and remaining migration effort.
We built an AI assisted migration factory around the existing engineering process.
The approach did not treat every mapping as an identical conversion task. Assets were analyzed, classified and routed through different levels of automation based on complexity and confidence.
AI assisted legacy ETL conversion
The migration factory combined legacy metadata analysis, AI assisted code generation, automated validation and engineering review.
- Parsed legacy mappings to identify sources, targets, joins, filters, lookups, expressions and dependencies.
- Used AI to explain legacy logic in plain language before conversion.
- Generated equivalent SQL and PySpark transformation patterns for the target Databricks or Fabric environment.
- Created migration documentation describing source fields, target fields and transformation rules.
- Generated test cases from mapping logic and expected transformation behavior.
- Compared source and target outputs using record counts, aggregates, key-level checks and data quality rules.
- Classified mappings by conversion confidence and routed complex exceptions to senior engineers.
- Tracked conversion status, validation results, exceptions and approvals through a centralized migration process.
The migration layer connects legacy metadata with modern engineering platforms.
AI is used as an analysis and generation layer. Data processing, validation and production execution remain within the governed target platform.
A six stage migration model designed for scale and control
Migration was organized into repeatable waves so the organization could increase automation without losing visibility over complex assets.
Discover
Inventory mappings, dependencies, transformation patterns, source systems and business ownership.
Classify
Group assets by complexity, repeatability, business criticality and conversion confidence.
Analyze
Use AI to extract logic, explain transformations and identify dependencies and exceptions.
Convert
Generate target SQL and PySpark and create associated documentation and test cases.
Validate
Reconcile source and target outputs and perform data quality, performance and functional checks.
Release
Complete engineering approval and deploy approved workloads through controlled production processes.
The model is designed to move migration work from manual rebuilding toward controlled automation.
Mappings addressed at scale
A factory based approach can manage a large mapping estate through standardized migration waves and progress tracking.
Standard conversion automation
Repeatable mappings can be candidates for AI assisted conversion, with complex cases routed for engineering review.
Migration effort reduction
Automating analysis, code generation, documentation and test creation can materially reduce manual conversion work.
Faster migration cycles
Reusable conversion patterns and automated validation can reduce the time required to complete each migration wave.
Validation before release
Every converted production asset can be required to pass defined functional and data validation checks.
Faster documentation
AI generated mapping explanations and transformation documentation reduce manual documentation effort.
Modernization becomes a repeatable delivery capability rather than a one time migration exercise.
The solution helps organizations reduce the effort associated with legacy migration while creating a stronger foundation for future data engineering and analytics workloads.
Lower Migration Cost
AI assisted analysis and code generation reduce the amount of engineering time required for repeatable conversion tasks.
Faster Platform Modernization
Migration waves can move faster because teams spend less time manually recreating standard transformation logic.
Better Migration Control
Classification, validation and exception tracking provide visibility into what has been converted, what passed validation and what still requires engineering work.
Reusable Modern Data Estate
Converted workloads can follow common SQL, PySpark, data quality and deployment patterns on the target platform.
Move from legacy migration projects to a repeatable modernization factory.
From legacy mapping analysis through target code generation, reconciliation and production release, AI can accelerate modernization while engineering teams retain control over quality and business logic.
Discuss your modernization program