Cloud data platforms promise elastic scale, lower maintenance and readiness for AI. Yet many migrations simply relocate old problems — and add a larger monthly bill.
1. Decide what to migrate, rebuild or retire
Start with an inventory of data sources, pipelines, reports and consumers. Many organizations find that a significant share of reports and tables are rarely used. Retiring them before migration reduces cost and risk.
2. Define the target architecture
Agree on storage, processing and serving layers, along with standards for naming, testing, security and cost management. A clear target state avoids recreating legacy complexity.
3. Migrate in value-driven waves
Group workloads into waves based on business value and dependency. Early waves should deliver visible improvements — faster reports, new data sources — to build momentum and confidence.
4. Run old and new in parallel, briefly
Automated reconciliation between legacy and new outputs builds trust. Set clear exit criteria so parallel running does not drag on.
5. Build cost management in from day one
Tagging, budgets, workload isolation and performance tuning keep consumption costs predictable. Review usage regularly as adoption grows.
6. Invest in people and process
New platforms need new skills and ways of working. Pair migration with training, documentation and clear ownership so teams can operate and extend the platform.
Planning a data platform migration? Talk to our cloud and data engineers.
