Zero-Copy Sharing with Analytics Hub Linked Datasets

What Analytics Hub and its core components are:

Analytics Hub is a fully managed BigQuery service that allows organizations to securely discover, share, and access data and analytics assets across organizational boundaries. Its core architecture consists of three key components:

• Data Exchange: A centralized, governed container where administrators manage visibility, listings, and member access.
• Listing: A reference published within an exchange that encapsulates a shared BigQuery dataset along with metadata, documentation, and policy controls.
• Linked Dataset: A read-only dataset created in the subscriber's Google Cloud project when they subscribe to a listing, acting as a direct pointer to the publisher's underlying storage.

How it meets the requirements:

• Centralized catalog (✓): Data exchanges and listings provide a structured, searchable catalog where publishers manage access and consumers discover relevant datasets.
• Direct consumer queries (✓): Consumers query the linked dataset directly within their own BigQuery environment using standard SQL syntax.
• Zero-copy infrastructure (✓): Subscribing creates a symbolic link rather than copying data blocks. Consumers read data directly from the publisher's BigQuery storage tier, avoiding duplicate storage costs and eliminating replication pipelines.
• Real-time consistency (✓): Because queries against linked datasets resolve directly against the live publisher dataset, any updates, inserts, or schema changes are immediately visible to consumers with zero lag.

Key advantages:

• Decoupled compute and storage billing: Subscribers pay for their own query compute processing costs while publishers retain storage ownership.
• Fine-grained security and governance: Publishers enforce IAM roles, row-level security, and column-level access policies that flow dynamically through the linked dataset.
• Zero operational overhead: Eliminates the maintenance of scheduled ETL jobs, export scripts, or API endpoints for data delivery.
• Cross-project and cross-organizational sharing: Supports seamless collaboration across separate organizations, projects, and geographic regions within BigQuery.

Why this is optimal:

This approach directly leverages BigQuery's separated compute and storage architecture to achieve true zero-copy data sharing. Alternatives that involve export files or replication pipelines introduce latency, synchronization overhead, and redundant storage expenses.