There are many benefits of a cloud migration. It significantly reduces your operation costs by utilising the pay-per-usage model. Additionally it reduces your downtime as platforms usually manage them internally and efficiently. It also allows you to be highly scalable for times when you expect a lot of traffic. Some platforms also provide auto-scaling feature. Your internal tech team can re-focus on building products rather than maintaining the server.

Cloud Migration is a strategy of its own which defines each and every step of the migration that is required and planned. If data migration is also a part of the plan, in that case multiple tests are performed before migrating the data to the cloud. There are many situations, where data is not touched and only the codebase is migrated to the cloud with or without optimisations.

Some of the top considerations for cloud migration include security assessment, pricing, scalability, data protection, and availability. Top cloud providers handles most of the security apart from setting up custom security in place. Pricing can be decided by doing a comparative study on the current vs cloud provider’s pricing modules. Scalability is a common performant for most of the cloud provviders, however if your traffic is very volatile and event driven, you should chose a platform that supports auto-scaling. Availiability is a also a common factor for most of the cloud platforms. However, some local and unknown cloud providers might have hidden downtime, make sure to know about them extensively.

When migrating to the cloud, make sure to determine strategy for each workload. There are 7 Rs to migrating which includes Refactor, Replatform, Repurchase, Rehost, Relocate, Retain, Retire. Migration strategy should be based on both the business and technical needs.

Data migration has 2 main approaches based on the usage of the product. Big Bang data migration approach is less costly and less complex but more risky. Here the entire data is migrated at one go by maintaining a downtime. If you decide to take this approach, make sure to test the approach multiple times before going for the production data migration. Trickle data migration approach is less prone to unexpected failures reducing risk, close to zero-downtime, but is more costly and more time consuming. In this approach, you run 2 parallel systems by syncing data in the new system for testing during the transition period. Extensive tests are needed to identify any loopholes. Only when both the systems, perform similar results with data handling, the system is then directed to the new data storage.