How would you handle data type changes for an existing column?

Handling data type changes for existing columns requires careful planning to avoid downtime and data loss. First, add a new column with the desired type alongside the existing one. Use CAST or CONVERT to populate it from the old column, handling edge cases (e.g., invalid dates, truncation). Validate the conversion with spot checks and row counts. Once validated, update downstream dependencies, then drop the old column and rename the new one. In production, use schema evolution tools (e.g., Delta Lake, Iceberg) that support add/rename with backward compatibility. For nullable conversions, use COALESCE or default values. Document changes and run migrations during low-traffic windows. Example: ALTER TABLE orders ADD COLUMN amount_new DECIMAL(10,2); UPDATE orders SET amount_new = CAST(amount_old AS DECIMAL(10,2)); ALTER TABLE orders DROP COLUMN amount_old; ALTER TABLE orders RENAME COLUMN amount_new TO amount; Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.