How do you troubleshoot the errors found during migration from legacy to modern data platforms?

First, isolate the error by reproducing it in a controlled environment. Check logs from both source and target systems, and use monitoring tools like Datadog or Azure Monitor to capture stack traces. Next, compare schemas and data types to identify mismatches; tools such as dbt or Great Expectations can automate schema validation. Perform data profiling to spot anomalies—missing values, outliers, or duplicate keys—that may cause failures. Use incremental migration to isolate problematic batches; if a batch fails, rollback to the last successful state. Leverage unit tests for individual transformation steps, and integration tests for end‑to‑end pipelines. If performance issues arise, analyze query plans in Spark or SQL engines, and adjust partitioning or caching. Finally, document the root cause and remediation steps, and update the migration playbook to prevent recurrence.