{"id":77119,"plugin_id":"plugins_6ab424ad5b448191b8fede2ebf569333","kind":"skill","collection_source":"plugin_package","comparison_source":null,"observed_at":"2026-11-03T00:12:27.229Z","digest":"6f7b159666a4c87c041c130935ec610470125839ba687b6adc89868ab5c4054c","against":null,"payload":{"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"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}