Consider a hypothetical scenario in which an online shopping platform has become extremely successful. It has outgrown its original database. This causes order histories to load very slowly.

Maintenance takes much longer than it used to. And engineers spend way too much time managing the workload and the system’s limitations. The engineering team behind this system wants to replace the database and migrate the data to a new database that promises better performance.

But you cannot take a popular online store offline to make the switch. Customers are always placing orders, changing addresses, and requesting refunds. The shop can’t be shut down just like that.

This scenario offers a glimpse of the main challenge inherent in production migrations. How do you change a working system while still running the service that people depend on? At scale, copying existing data during such migrations can take days. Also, many supporting applications may depend on the component being replaced.

Every intermediate step needs to work while ordinary business continues. In this article, we will look at how migrations work at scale and the key strategies that can help make it as efficient as possible.