Point-in-Time Restore vs. Long-Term Retention for Failed Schema Upgrades

When managing a failed schema upgrade in Azure SQL Database, the recovery strategy depends on the discovery window of the failure. Point-in-Time Restore (PITR) provides millisecond granularity for immediate reversals, while Long-Term Retention (LTR) backups serve as a fallback for errors discovered after the standard retention period has expired.

A critical constraint is that neither method performs an in-place overwrite; both create a new database instance. This necessitates a decision between the speed and precision of PITR and the extended safety net of LTR, especially when dealing with massive datasets where the restoration time may impact the overall recovery time objective (RTO).

• Does the restoration of a massive dataset via PITR impact the performance of other databases on the same logical server?
• In high-tier service levels, what are the specific trade-offs in RTO when choosing LTR over PITR for a multi-terabyte recovery?