An enterprise data architecture team is configuring BigQuery Analytics Hub to publish core multi-terabyte datasets to multiple internal and external subscriber projects. The leadership team requires a clear financial governance model and audit strategy that meets the following criteria: Storage Costs: The publisher project maintains and pays for the primary storage of the underlying shared datasets. Compute / Query Costs: Each subscriber project is financially accountable for its own analytical compute and query processing costs when querying shared linked datasets. Auditing & Monitoring: Table access metadata must be audited centrally in the publisher project, while subscriber projects retain the ability to monitor their own query latency, bytes scanned, and slot utilization. Which architecture and monitoring configuration aligns with BigQuery Analytics Hub best practices and financial governance models?

The publisher hosts and pays for the BigQuery storage in the source project and publishes listings via Analytics Hub; subscribers create linked datasets in their respective projects and pay for their own query compute (on-demand or BigQuery Editions slot reservations). The publisher monitors table access and data access patterns using Cloud Audit Logs in the publisher project, while subscribers analyze query execution performance and slot consumption using their own project's INFORMATION_SCHEMA.JOBS_BY_* views.: What BigQuery Analytics Hub Data Sharing Is BigQuery Analytics Hub is a fully managed data-sharing service that allows organizations to securely exchange datasets, views, and machine learning models across organizational boundaries without copying or moving the underlying storage. How it Meets the Financial and Governance Requirements Storage Costs Separation: The publisher project maintains the authoritative table data and incurs the BigQuery storage pricing (active and long-term logical or physical storage fees). Subscribers reference the data via linked datasets without creating independent storage copies. Compute / Query Costs Allocation: When a subscriber executes queries against a linked dataset, the compute pricing (either per-TB on-demand processing or BigQuery Editions capacity slot reservations) is charged directly to the subscriber's billing account and project. Centralized and Subscriber-Level Auditing: The publisher tracks dataset and table reads using Cloud Audit Logs (Data Access logs) in the publisher project to audit which principals are accessing the underlying data. Concurrently, subscribers monitor their own query runtimes, bytes billed, and slot resource consumption using project-level INFORMATION_SCHEMA.JOBS_BY_PROJECT and INFORMATION_SCHEMA.JOBS_BY_USER views. Key Advantages Zero Data Duplication: Eliminates ETL/ELT pipelines and associated egress and duplicate storage expenses. Decoupled Financial Model: Automatically isolates query execution charges to the consumers of the data. Granular Security and Access Auditing: Preserves publisher row- and column-level security policies while providing complete audit trails in Cloud Logging. Independent Performance Tuning: Subscribers can allocate dedicated slots or reservations to optimize their analytical workloads without impacting the publisher's compute resources. Why This Is Optimal This architecture directly implements Google Cloud's native in-place data-sharing model. It satisfies all cost-allocation requirements out of the box while providing robust auditability via Cloud Logging and INFORMATION_SCHEMA.