For migrating a Python/Pandas-based compliance model from AWS SageMaker/Airflow to Azure Databricks/ADLS, the best approach depends on priorities like speed, risk, cost, and future scalability. Both "Lift & Shift" and "Refactor & Modernize" have clear trade-offs that should be evaluated in the context of your business needs and long-term goals.
Lift & Shift
-
Pros
-
Fastest migration: You can quickly get your existing code running with minimal changes, allowing for rapid testing and deployment on Azure Databricks.
-
Lower immediate risk and effort: Familiarity with the existing codebase makes troubleshooting easier.
-
Simplified rollback: If issues arise, you can revert to AWS more easily since core functionality remains unchanged.
-
Cons
-
Limited scalability: Pandas operations are in-memory, which doesn't leverage Spark's distributed computing.
-
Higher long-term operational costs: Databricks pricing is better optimized for Spark workloads, not for Pandas jobs.
-
Missed Azure-native optimizations: You won’t benefit from performance improvements and integrations available with Spark, ADLS, or MLflow.
Refactor & Modernize
-
Pros
-
Performance boost: Migrating Pandas code to PySpark or Koalas unlocks better scalability for large datasets through distributed processing.
-
Enhanced maintainability: Aligning with Databricks best practices and Azure-native features supports future-proofing and easier integration with ADLS, Delta Lake, and MLflow.
-
Cost-effectiveness at scale: More efficient resource utilization on Databricks and easier management of data pipelines.
-
Cons
-
Increased migration time and complexity: Significant code rewrites, team upskilling, and validation effort required.
-
Higher upfront investment: More design, testing, and documentation compared to Lift & Shift, delaying deployment and ROI.
Recommendation
If your goal is rapid migration and validation, Lift & Shift is reasonable for initial deployment, especially if you’re constrained by time or resources. However, for sustainable operations and scalability, Refactor & Modernize offers better long-term value by fully leveraging Azure Databricks and ADLS capabilities. Many organizations blend both strategies: start with Lift & Shift for quick wins, then iteratively refactor critical components to Spark/Delta Lake as business needs grow.
Key Advice
-
Assess data size, workflow complexity, and compliance requirements before deciding.
-
Identify high-value pipelines or bottlenecks for targeted modernization.
-
Plan for change management, team training, and thorough testing.
-
Document the migration approach for auditability and future optimization.
By balancing immediate needs with long-term strategy, you can minimize disruption while maximizing your investment in Azure’s ecosystem.
