You need a data-mesh architecture to share data between sales, product, and marketing across Cloud Storage and BigQuery. How should you organize projects, storage, datasets, and sharing to enable departmental ownership and discovery?

The correct answer aligns with data mesh principles by promoting domain-oriented ownership and decentralized data products. Creating multiple projects per department allows for clear resource isolation and departmental autonomy over their data infrastructure (Cloud Storage buckets and BigQuery datasets). Dataplex is crucial for discovery and governance, mapping departmental data lakes (Cloud Storage) and BigQuery datasets to zones, enabling a unified view while maintaining distributed ownership. This structure facilitates self-service data consumption and sharing, a core tenet of data mesh. Incorrect options: Option 1 (IT-administered groups) centralizes control, hindering departmental ownership and agility. Option 2 (Analytics Hub) is primarily for external data sharing and subscription, not internal departmental ownership and management of data products within a data mesh. Option 3 (single storage project, central bucket/dataset) creates a monolithic architecture, directly opposing the decentralized nature of a data mesh.