1. Inventory and Classify Your Pipelines
- Identify which jobs are business-critical, which are tactical, and which can be retired.
- Group pipelines by domain (payments, policy, claims, etc.) to plan migration waves.
- Flag pipelines with frequent incidents or known quality issues as high-risk.
2. Assess Code Quality Before You Move
- Use automated static analysis to understand current code quality.
- Identify technical debt hotspots and anti-patterns that should be refactored.
- Decide which jobs can be migrated “as is” versus which need redesign.
- Document core business rules separately from implementation details.
- Avoid re-creating proprietary quirks or workarounds from legacy tools in the new platform.
- Standardize how you express transformations across platforms where possible.
4. Redesign for Cloud-Native Architecture
- Use cloud storage (data lake or object storage) as the central hub for data.
- Leverage native compute engines (Spark, SQL pools) for heavy transformations.
- Design with elasticity in mind—assume volume growth and variable workloads.
5. Build in Observability and Housekeeping from Day One
- Define standard logging, metrics, and error handling across all new pipelines.
- Agree on restart patterns and replay strategies as part of the design, not as an afterthought.
- Ensure housekeeping (cleanup, archival) is included in the solution, not left “for later.”
6. Plan Parallel Runs and Cutover
- Run legacy and cloud pipelines side-by-side for a defined period.
- Use data reconciliation and sampling to validate that results are equivalent.
- Define clear decision criteria for cutover and rollback.
Code quality is a major predictor of modernization success. By combining automated analysis (via a platform like Undraleu) with disciplined design practices, you reduce surprises, shorten timelines, and end up with a cloud data platform that is easier to operate and evolve.