I jumped straight into talking about batch jobs and CDC (change data capture) without first clarifying what 'System Y' actually needed.
Start by clarifying requirements: data volume, velocity, consistency needs, and downtime tolerance. Then propose a phased migration strategy using dual-writes or change data capture (CDC) to keep systems in sync, with validation and rollback plans. Finally, discuss trade-offs between approaches and how you would handle schema differences and data transformation.
Pro tip: Emphasize idempotency and exactly-once semantics in your migration pipeline to avoid data duplication or loss, and always include a rollback strategy. Mention that you would monitor lag and data consistency metrics throughout the migration.
Ask about data size, write throughput, acceptable downtime, consistency requirements, and target system capabilities. This shapes the migration approach.
Decide between offline (batch) vs online (live) migration. For online, consider dual-writes, CDC, or a combination. Discuss trade-offs like complexity, latency, and consistency.
Outline how data will be read from Cassandra (e.g., using Spark or custom readers), transformed to fit the target schema, and written to the new system. Address idempotency and error handling.
Implement validation checks (e.g., row counts, checksums) and reconciliation processes. Plan for handling discrepancies and ensuring eventual consistency.
Define steps to switch traffic to the new system, including a rollback plan if issues arise. Consider a gradual cutover with canary testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.