Cloud Migration

Definition

Cloud migration is the process of moving applications, data, and workloads from on-premises infrastructure (or one cloud provider) to a public cloud platform such as AWS, Azure, or Google Cloud.

Overview

Cloud migration covers everything from moving a single database to a managed service, to relocating an entire data center's worth of applications onto AWS, Azure, or Google Cloud. Because the scope and risk vary so much by workload, most large migrations are planned using a framework rather than moved all at once — AWS, for example, popularized the "6 R's": Rehost (lift-and-shift a VM as-is), Replatform (make small optimizations during the move, like switching to a managed database), Repurchase (replace with a SaaS equivalent), Refactor/Re-architect (redesign for cloud-native patterns), Retire (decommission workloads that are no longer needed), and Retain (leave certain systems in place for now). A typical migration starts with discovery and assessment — cataloging existing applications, their dependencies, and their data volumes — followed by choosing a strategy per workload, often starting with lower-risk, less business-critical systems as a proof of concept. Data migration itself is frequently the hardest part: large datasets may need dedicated transfer services (like AWS Snowball or Azure Data Box for physical transfer, or database migration services for live replication) to avoid unacceptable downtime or bandwidth costs. Migration is often just the first step toward a broader modernization effort — many organizations rehost first to get out of a data center quickly, then incrementally refactor toward cloud-native architecture once workloads are already running in the cloud. Because migrations reshape an organization's cost structure, they're closely tied to FinOps planning, and the Terraform & Infrastructure as Code course covers the tooling commonly used to provision the target environment.

Key Concepts

• Structured around strategies such as rehost, replatform, repurchase, refactor, retire, and retain
• Begins with discovery and assessment of existing applications and dependencies
• Often phased, starting with lower-risk workloads before critical systems
• Dedicated data-transfer tooling for large datasets (physical appliances or live replication)
• Frequently paired with cost modeling to compare on-premises versus cloud total cost of ownership
• Can be a stepping stone toward broader cloud-native modernization

Use Cases

Frequently Asked Questions

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