GovTech Cloud Legacy Migration & Retention Engine
Modernizing legacy public sector data storage requires automated mapping of legacy database schemas to cloud-native, compliant storage backends. Build a legacy migration translation engine that parses cold storage schemas and generates secure DuckDB migration scripts. Achieve 100% schema parity and regulatory retention flag preservation.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an automated data schema migration and retention tag mapping engine for public sector cloud modernization.
How work is evaluated
Evaluates schema parity and target script execution correctness in DuckDB.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
duckdb_execution_check
Ensures generated SQL executes without error in DuckDB.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
type_conversion_accuracy
Percentage of correctly mapped column data types. • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Parse legacy DDL statements (Oracle/DB2 dialect) into normalized ASTs
Generate target DuckDB and Parquet schema definition scripts
Automate application of statutory data retention metadata tags
Validate data type parity and losslessness across migration steps
Reference links and supporting material
Synthetic legacy database DDL definitions from legacy government administrative applications.
Challenge setup
Evaluates schema parity and target script execution correctness in DuckDB.
Challenge input
Legacy DDL string
DuckDB
Fast in-process SQL database.
Evaluated output
Target DuckDB SQL string
- Ensures generated SQL executes without error in DuckDB.
- Percentage of correctly mapped column data types. • target: 1 • range: 0-1
- Benchmark: DDL-Transpile-Bench
- Type Conversion Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Skyvern · Required
- Weights & Biases · Optional
- Distributional · Optional
- Distributional · Optional
Evaluation contract
- duckdb_execution_check · Weight 1
- type_conversion_accuracy · Weight 1
Metrics
- type_conversion_accuracy · Target 1
Benchmarks
- DDL-Transpile-Bench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: Legacy DDL string
Output: Target DuckDB SQL string
Execution harness: Python
Evaluation tools
- DuckDB · Target Analytical Engine
Recipe freeze marker
No recipe freeze time is recorded.