Efficiency up, failed releases down; compliant production data for your test and dev environments
summary
A real pipeline for usable, compliant test data: classify sensitive columns with Redgate Data Catalog, export a mapping file, apply rules and static masking with Data Masker (keeps referential links and substitutes realistic emails/names), then build a single masked image and deliver writable clones with SQL Clone so teams avoid repeated 10‑hour restores. Anecdote: a deployment once ran queries for three days because dev used one‑row test data; this process preserves distributions, scales to 64 TB, and produces repeatable, auditable anonymized copies.
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chapters (14)
- 0:00Compliant production data intro
- 3:19Why production data is risky
- 6:55Realistic test data role
- 9:05Identifying and cleaning data challenges
- 12:42Data management hurdles
- 18:27Tagging data for compliance
- 26:12Never use production data directly
- 27:14Generated vs masked data
- 30:06Large DBs in development
- 32:37DB sharing limits
- 35:44Risks of unmasked data
- 38:40Catalog and masking tools
- 40:46Demo: Masking and cloning DBs
- 47:14Summary and Q&A
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