I see it in 4 steps: 1. Connect to a source db to import your data 2. Train a Gen AI using the source data 3. Use it create synthetic data 3. Export synthetic data into a new db
The SDV team is working on business solutions to cover the full use case. You can use the public SDV to validate steps 2 and 3.
1. spin up a production-equivalent database (eg: mysql container instead of prod RDS)
2. point a process/binary (maybe a simple container) to:
-- source db (RDS)
-- sink db (mysql container)
-- transformation function (that may use gen AI, etc) to seed sink db with synthetic/anonymized data [there may be some parallel process to enable testing of this transformation function]
3. profit (use this for dev etc)
Key over here would be speed in step (2) if the entire pipeline were to run end-to-end on-demand. do you have some examples of using SDV to achieve this? highly possible that there's already something in the docs that I have missed