A data platform migration means moving data, systems and applications to another data platform. In many cases, this is a cloud-based or more modern platform. Organisations are increasingly opting for this to cut costs, work faster or improve collaboration between teams.
Such a migration can be complex, but with the right approach, it delivers a lot. In this article, you will read how to perform a data platform migration in a controlled way, safely and without surprises.
Challenges of a data platform migration
A migration is rarely a matter of ‘just transferring’. You run into challenges such as:
- Complexity: A migration forces you to take a critical look at outdated processes. What is still relevant, what can go away? At the same time, you want to connect to the latest technological developments, such as AI applications or real-time processing. This requires choices and a well-considered design, so that old and new components will soon work well together in one future-proof platform.
- Data loss: If structure or formats are not properly matched, information can be lost.
- Security and privacy: Data must remain secure throughout, including during transport and processing.
- Downtime: Temporary system downtime can disrupt operations.
Without proper preparation, such risks can quickly grow into bigger problems.
Roadmap for a successful migration
A structured approach makes all the difference. For this, the following steps are important:
- Analyse and plan
Map all dependencies and data flows. Determine which data are relevant to the new platform and remove what is redundant. Use this moment to already clean up your data and make it suitable for the new environment. Then draw up a detailed migration plan, including timeline, approach and fallback scenarios.
- Redesign and prepare
Redesign your data architecture where necessary and make sure the data are cleansed, structured and compatible between the old and new environment. This will prevent disruptions for end users and ensure process continuity. Then configure the required cloud resources, security settings and access rights for the new platform.
- Migrate step by step
Perform the migration to production in phases so that you can move to the desired new situation step by step. Always work with separate test, acceptance and production environments so that end users can test early and habituation takes place safely.
- Test continuously
Validate data and functionality at every stage of the migration. Automate checks and involve key users for acceptance testing to detect errors early.
- Build in quality checks
Implement automatic quality checks in the pipeline, such as completeness and logical values. This ensures quick error detection and reliable data towards end users.
- After-care and optimisation
Verify together with stakeholders whether everything has been migrated correctly and update processes. Offer training to users and monitor the new platform for optimisation.
Types of migrations
There are different types of migrations:
- Tool migrations: For example, from Dataiku to Databricks. Read 5 lessons from such a migration here.
- Cloud migrations: Such as moving from SAP BW to Azure or Databricks.
- Hybrid migrations: In which you partly retain old infrastructure.
Whichever route you choose: our experience has shown that a migration to Databricks is almost always possible and scalable.
Common mistakes in migrations
What often goes wrong:
- Too little preparation: No clear scope or test plan.
- Insufficient business involvement: IT does not solve it alone.
- Too little monitoring during the migration: Problems only become visible after going live.
- No rollback plan: What if things go wrong?
By arranging this in advance, you avoid many problems afterwards.
How can Blenddata help?
At Blenddata, we have years of experience with complex migrations, from proof-of-concept to fully operational platform. We think not only technically, but also about governance, management and adoption.
What you can expect:
- A good inventory: what do you really need?
- Future-proof solutions.
- Scalable platforms, ready for growth.
- Short lines of communication with experienced engineers, no unnecessary layers.
- Guidance from A to Z, tailored to your organisation.
Whether you switch from Dataiku, SAP or another platform: we make sure your data keeps working. And your organisation performs better.
