The short answer. Data migration moves data from one system to another, once, as a project with an end. Data integration keeps data flowing between systems, continuously, as an ongoing capability. Migration is a move; integration is a connection. Most enterprises need both, but at different moments and with different tools.

These two terms sit close together and often get swapped in conversation. The distinction matters, though, because it changes what you build, how long it runs, and how you measure success.

What data migration is

Data migration is the one-time transfer of data from a source system to a target, usually as part of a larger change: replacing a legacy platform, moving to the cloud, or consolidating systems. It has a clear beginning and end. The classic example today is moving from SAP ECC to S/4HANA, where data has to be extracted, cleaned, transformed to a new model, loaded, and reconciled. When the target is live and verified, the migration is done.

What data integration is

Data integration is the ongoing process of combining and synchronising data across systems so they work together. It does not end. A CRM feeding an analytics warehouse, an e-commerce platform syncing with an ERP, applications sharing a common view of a customer: these are integration problems. The goal is a continuous, reliable flow, not a single move.

Migration vs integration at a glance

Aspect	Data migration	Data integration
Purpose	Move data to a new system	Keep data flowing between systems
Duration	One-time project with an end	Continuous, ongoing
Typical trigger	System replacement, cloud move, consolidation	Connecting apps, analytics, real-time sync
Success looks like	Target live and reconciled	Reliable, current flow over time
Data shape	Transformed once to the target model	Mapped and kept in sync repeatedly

When you need each

You need migration when you are retiring or replacing a system, moving to S/4HANA, adopting the cloud, or consolidating landscapes after a merger.
You need integration when systems that will keep running have to share data, feed analytics, or stay synchronised in near real time.
You often need both in sequence: migrate to the new platform, then integrate it with the surrounding landscape so it keeps working.

Where they overlap

Both rely on the same underlying discipline: extracting data, transforming it to fit a target, validating it, and moving it reliably. That shared foundation, ETL, is why the same platform can support both. The difference is not the mechanics; it is whether the job runs once or forever.

Where the confusion causes real problems

Mixing up the two is not just a vocabulary issue; it leads to concrete mistakes:

Buying the wrong tool. A team that treats a one-time migration as an integration problem may invest in continuous sync infrastructure it does not need, or vice versa.
Setting the wrong expectations. Leaders told they are "integrating" may expect an ongoing capability, when what is really being delivered is a one-time move that ends.
Under-scoping the data work. Migration demands a heavy one-time cleanse and transform; integration demands durable, repeatable mapping and monitoring. Plan for the wrong one and the effort lands in the wrong place.

A worked example

Picture a manufacturer moving from SAP ECC to S/4HANA while also running a separate analytics platform. The move to S/4HANA is a migration: extract from ECC, clean and de-duplicate, transform to the new model, load, reconcile, done. Feeding the analytics platform from S/4HANA afterward is integration: a repeatable flow that keeps the warehouse current as new transactions occur. Same manufacturer, same data, two different jobs, running at different times with different definitions of success. Recognising which is which keeps both on track.

How they fit into the data lifecycle

It helps to see migration and integration as different moments in the life of a system rather than rival approaches. Migration is a birth event: it happens once, when data moves onto a new platform, and it ends when that platform is live and verified. Integration is the ongoing metabolism: it keeps data flowing in and out of that platform for as long as the system lives. A system is migrated once but integrated continuously.

This is why the skills and tools overlap but the mindsets differ. A migration team is racing toward a finish line and optimising for a clean, complete, one-time move. An integration team is building something to run indefinitely and optimising for reliability, monitoring, and graceful handling of the inevitable changes over time. Confusing the two leads to migration teams building for permanence they do not need, or integration teams treating a permanent flow as if it were a one-off.

Getting both right in a single programme

Large transformations usually involve both at once, which is where clarity pays off. A move to S/4HANA is a migration, but the new system rarely stands alone; it has to exchange data with analytics platforms, customer systems, and partners, which is integration. Planning them as distinct workstreams, with distinct definitions of done, keeps each honest. The migration workstream finishes when the target is reconciled and live. The integration workstream never really finishes; it transitions into run-and-maintain. Naming them correctly from the start avoids the awkward moment when a programme declares victory on a migration only to realise the integrations that make the system useful were never scoped.

The tools reflect the difference

Because the two jobs differ, the tooling tends to emphasise different things. Migration tooling is built around a one-time, high-stakes move: rich extraction from a specific legacy source, heavy transformation to a target model, staging, and reconciliation to prove the result. Integration tooling is built around durability: connectors that stay live, scheduling, monitoring, and error handling for a flow that must keep running unattended. The shared core is ETL, which is why a single platform can serve both, but the surrounding features reveal which job a tool was designed for. When evaluating any tool, it is worth asking plainly whether you are buying it to finish something or to run something, because that question decides which capabilities actually matter to you.

How deKorvai helps

deKorvai provides end-to-end ETL: extraction from source systems, rule-driven transformation and validation, and reliable loading into target platforms. For a one-time move, that supports a full S/4HANA migration with profiling and reconciliation. The same extraction, transformation, and validation capabilities apply wherever data has to move accurately, whether the job runs once or on a schedule.

Key takeaways

Migration is a move with an end; integration is a connection that continues.
Different success measures: a reconciled target vs a reliable ongoing flow.
Shared foundation: both are built on ETL, so one platform can serve both.
Often sequential: migrate first, then integrate the new system.

Frequently asked questions

Is data migration the same as data integration?

No. Data migration is a one-time move of data to a new system and then it is finished. Data integration is a continuous flow of data between systems that keep running. They use similar techniques but solve different problems.

Is ETL migration or integration?

ETL (extract, transform, load) is the underlying technique both rely on. It powers a one-time migration and an ongoing integration alike. The difference is whether the ETL job runs once or repeatedly.

Do I need both?

Often, yes, but at different times. A typical pattern is to migrate to a new platform such as S/4HANA, then integrate that platform with the surrounding systems so data keeps flowing afterward.

Which comes first?

Usually migration. You move onto the new system, verify it, then build the integrations that keep it connected to the rest of your landscape.

Data Migration vs Data Integration: Different Jobs, Different Tools