When planning a data warehouse migration that must preserve data integrity and cannot afford significant downtime.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Databases & Data Engineering - Use case: Data Warehouse Migration Plan - Source task: - I need to migrate data from {{source_legacy_database_on_premise_warehouse_old_vendor}} to {{target_snowflake_bigquery_redshift_new_schema}}. - Step 1: Assessment: audit the source data (volume, quality, dependencies, undocumented business logic in queries). - Step 2: Migration Design: choose migration approach (big bang vs. incremental), data transformation rules, and cutover strategy. - Step 3: Validation: design a validation framework to verify data integrity after migration. - Step 4: Rollback Plan: how to revert if the migration fails. # Goal Source data audit, migration design with cutover strategy, data integrity validation framework, and a rollback plan. # Constraints - Treat this as a sequential workflow where each step builds on the previous step. - Keep every step clearly labeled and easy to run separately if needed. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output Source data audit, migration design with cutover strategy, data integrity validation framework, and a rollback plan.
{{double-curly}} with your real context.When planning a data warehouse migration that must preserve data integrity and cannot afford significant downtime.
Migrate the metadata (schemas, views, stored procedures) before the data — every undocumented stored procedure is a surprise waiting to ruin your timeline.
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