Cloud migration and lakehouse transitions are some of the most common platform migrations organizations undertake today. The same principles that make Data Mesh migrations work—incremental transition, maintaining operational continuity, and focusing on business value—apply equally to these architectural shifts.

Universal Migration Principles (quick checklist)

Cloud Migration Journey

Many organizations start on-premise and move to cloud to improve scalability, enable modern ML workloads, and increase speed of delivery. But cloud migration introduces its own risks: cost surprises, skill gaps, and architectural sprawl.

Key drivers for change

Cost transparency is often a major driver, especially when budgets tighten. By federating platforms and enabling teams to own their resources, organizations can achieve clearer cost allocation and better financial control.

Another common driver is reducing vendor lock-in. Moving toward open source technologies and avoiding heavy reliance on a single cloud provider increases flexibility.

Lakehouse Architecture Transition

The lakehouse pattern emerged as a response to two extremes:

A lakehouse aims to combine lake flexibility with warehouse reliability by leaning on open table formats and stronger metadata management.

Technologies often associated with this shift include Delta Lake, Apache Iceberg, and Apache Hudi, which bring stronger schema enforcement and improved quality guarantees on top of lake storage.