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| Decision | Rationale | |----------|-----------| | | Business users already store files in Drive; no extra ingestion pipeline needed. | | Akka as the message backbone | Provides exactly‑once delivery, location transparency, and built‑in supervision – essential for ML pipelines that must not silently drop data. | | Mashi 7 for catalog & scheduling | Its visual UI lets non‑technical stakeholders understand data lineage; its SDK allows us to embed custom Akka‑based logic without writing a separate scheduler. | | Mage for pipeline logic | Data scientists stay in familiar Python, while Mage’s artifact caching prevents re‑computations when only a subset of steps changes. | | Event‑sourced audit trail | Guarantees traceability and makes rollback to a previous pipeline version a single‑click operation. | mage+akka+mashi+7+google+drive+new

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