What Is Cloud Migration?

Cloud migration is the process of moving an organization's data, applications, and workloads from on-premises systems (or one cloud) to a cloud computing environment such as AWS, Azure, or Google Cloud. For data teams specifically, it usually means migrating from a legacy on-prem warehouse to a cloud data platform like Snowflake, Databricks, or BigQuery - and rebuilding the pipelines, reports, and models that depend on it.

It matters because migration is one of the highest-stakes projects a data organization undertakes: it touches every system, every pipeline, and every report at once, and it is notoriously prone to overrunning budget and timeline. The promise - elasticity, lower operational burden, modern analytics and AI - is real, but it is realised only if the migration preserves what made the old estate trustworthy: the meaning of the data, its quality, and the dependencies between systems. Migrations that move the data but lose its context simply rebuild the old chaos in a more expensive place.

Cloud Migration Defined

Migration is not a single act but a transformation program. At minimum it moves data and compute to a new platform; at most it re-architects how the organization works with data entirely - adopting a lakehouse, a data mesh, or a modern ELT stack. The scope chosen determines both the value captured and the risk taken on.

The reason migrations are hard is rarely the data movement itself - copying bytes is a solved problem. The difficulty is everything attached to the data: the hundreds of pipelines that transform it, the reports and models that consume it, the business logic encoded in legacy SQL, and the institutional knowledge of what each table actually means. A migration succeeds or fails on how well that web of dependencies is understood before the move.

The Six Rs of Migration

A widely used framework (originating with Gartner and popularised by AWS) classifies migration strategies by how much you change as you move. Each trades effort for cloud-native benefit:

• Rehost ("lift and shift"). Move as-is to cloud infrastructure. Fastest and lowest-risk, but captures little cloud-native value.
• Replatform ("lift and reshape"). Move with minor optimizations - e.g. swapping a self-managed database for a managed one.
• Refactor / re-architect. Redesign for the cloud - adopting a lakehouse, ELT, serverless. Highest effort, highest reward.
• Repurchase. Replace a system with a SaaS equivalent.
• Retire. Decommission what is no longer needed - and a migration is the ideal moment to find it.
• Retain. Deliberately keep some systems where they are (often for sovereignty or cost reasons), producing a hybrid estate.

Most real migrations apply several Rs across different systems. Choosing well requires knowing which systems matter and what depends on them.

Data Migration Phases

A disciplined data migration runs in four phases. The first is the one teams are tempted to rush - and the one that determines success:

1. Assess. Build a complete inventory of what you have, what it means, and - critically - how everything connects. This is where lineage earns its keep: you cannot safely move a table without knowing which pipelines and reports depend on it. Assessment is also when you find the dead datasets to retire rather than pay to migrate.
2. Plan. Choose a strategy (the 6 Rs) per system and sequence the move into waves, ordered by dependency so nothing is migrated before what it relies on.
3. Migrate. Move the data and rebuild the pipelines, transformations, and reports on the new platform - ideally in waves, not a single big bang.
4. Validate. Reconcile the new estate against the old: do the numbers match, are the reports identical, did anything quietly break? Parity must be proven, not assumed.

Where Migrations Fail

Almost every painful migration shares the same root cause: moving data without knowing what depends on it. The symptoms are familiar - a dashboard that goes blank because a source table was migrated late, a downstream model trained on a column that was renamed in transit, a report that "still works" but is now subtly wrong because a transformation was rebuilt incorrectly. These are schema-drift and dependency failures, and they are invisible until someone notices the numbers are off - usually after go-live.

The second great failure is migrating the mess: lifting and shifting thousands of undocumented, low-quality, duplicated datasets into the cloud, where they cost more to store and remain just as untrustworthy. A migration is the rare opportunity to not bring the chaos along - but only if you can see what is worth keeping.

How Dawiso De-Risks It

Both failure modes are visibility problems, which is exactly what a governed catalog solves. Dawiso gives a migration the two things it most needs. First, a complete, automatically discovered inventory of the source estate - so the assess phase produces a real map instead of a hopeful guess, and dead data can be retired rather than migrated. Second, interactive data lineage that shows every dependency across systems - so you can sequence the migration by what feeds what, predict the blast radius of moving any asset, and at the end prove parity by comparing source and target lineage. The catalog and its business context also carry forward the meaning of the data, so the new platform inherits a documented, governed estate rather than a pile of unlabeled tables. Migration stops being a leap of faith and becomes a mapped, staged, verifiable project.

Conclusion

Cloud migration promises elasticity, lower operational burden, and modern analytics - but it delivers them only to teams that move deliberately. The strategy framework (the 6 Rs) and the phased approach (assess, plan, migrate, validate) matter, but the decisive factor is visibility: knowing what you have and what depends on it before you touch anything. Migrations fail in the dark and succeed in the light. Map the estate, trace the lineage, retire the dead weight, and validate against the source - and the move to the cloud becomes an upgrade rather than a gamble.