Data Migration and Consistency Questions
Plan and execute data migrations while preserving correctness and availability. Topics include zero downtime migration techniques, schema evolution patterns, backward and forward compatibility, dual writes and shadow writes, incremental and bulk migration strategies, data validation and reconciliation, canary migrations, rollbacks and fallback plans, and how to minimize user impact during transitions. Understand trade offs between consistency and speed of migration and techniques to detect and correct drift after migration.
HardBehavioral
0 practiced
Describe a time when a migration you were responsible for encountered a major failure or data incident. How did you lead the team through triage, client communication, remediation, and the post-mortem? Emphasize decision-making, communication cadence, and lessons you enforced afterward.
EasyTechnical
0 practiced
A small schema change requires renaming column `email` to `contact_email` which is used by several external APIs. Outline a low-risk migration approach to make this change with minimal user impact and explain how you'd handle clients still reading `email` vs. those using `contact_email`.
HardTechnical
0 practiced
Design a reconciliation algorithm/system that detects and resolves conflicts between source and target when the migration included non-deterministic transformations (e.g., derived scores using external ML services). Describe how to identify conflicts, rank corrective actions (auto-fix, manual review), and maintain auditability.
MediumTechnical
0 practiced
You need to migrate parent (accounts) and child (transactions) tables in batches. Describe an algorithm to migrate in batches while preserving referential integrity and minimizing locking, including how you handle concurrent writes and foreign-key constraints during the migration.
EasyTechnical
0 practiced
Explain what a canary migration is and describe a practical canary plan for migrating customer profile reads to a new read-replica for 1% of traffic. Include how you'd select the canary cohort and which metrics would determine success before scaling to 100%.
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