Update to Data Engineering Copilot

{
  "description": "Migrate legacy ETL, Spark, schedulers, cloud warehouses or lakehouse code while preserving correctness and contracts.",
  "included_files": [],
  "name": "modernize-data-platform",
  "skill_md_contents": "---\nname: modernize-data-platform\ndescription: Migrate legacy ETL, Spark, schedulers, cloud warehouses or lakehouse code while preserving correctness and contracts.\n---\n\n\n# Modernize an existing platform\n**Trigger:** Upgrade Spark/Databricks/Airflow, migrate SSIS/legacy ETL to Fabric, replatform warehouses or refactor existing code.\n\n1. Inventory current source systems, exact interfaces, consumers, success metrics, historical data, dependencies and run cadence; inspect code before proposing a rewrite.\n2. Create a baseline of output row counts, grain, key uniqueness, totals, freshness, performance, failure/retry behavior and security access.\n3. Establish equivalence gates and source-of-truth. Map transformations and null/date/precision semantics across dialects and engines.\n4. Plan shadow runs, bounded backfills, checkpoint and state compatibility, lineage cutover, rollback and concurrent-writer handling.\n5. Verify each migration increment, reconcile historical/late/deleted data, and keep legacy production path available until gated cutover.\n6. Label expected behavior vs verified results. Avoid claiming all migrations preserve semantics automatically.\n\nUse `../../references/migration-playbook.md`, `../../references/testing-standards.md`.\n"
}