The Data Gap
You cannot test mainframe migration with "Fake" data (John Doe, 123 Main St). You need "Real" fake data—data that preserves the statistical distribution, edge cases, and dirty inputs of the production database, without containing a single real customer record.
We train a GAN (Generative Adversarial Network) on the air-gapped mainframe data. The output is a dataset that mathematically mirrors production but contains zero PII (Personally Identifiable Information).
Generation Protocol
GAN_TRAINER
01$> epoch_start --n=500
02Ingesting schemas: CUST_DB, TRAN_LOG...
03Detecting outliers in Transaction_Amount...
04PII Scrubbing: Active (Regex + NLP)
05Generating 1M rows...
06Validation: Statistical variance < 0.01%
07Output: test_data_v4.sql
08
Use Case
This module solves the "Developer Bottleneck." Instead of waiting weeks for compliance approval to access data, developers pull a docker container pre-loaded with this synthetic dataset and start coding immediately.