A retail enterprise wants to establish a decentralized data-sharing architecture to publish curated demand-forecasting datasets and file assets to hundreds of vendor partners and internal domain teams. The data architecture team needs to implement a solution that fulfills the following operational and financial requirements: Provide centralized discoverability and rich metadata annotations across all published SQL datasets and storage filesets. Enforce a cost-sharing model where the publishing team pays only for underlying data storage, while consumers incur the compute and query execution costs. Eliminate unnecessary data replication and maintenance overhead across consumer projects. Which combination of Google Cloud services and configurations should the enterprise implement?

Catalog the assets using Dataplex Universal Catalog, publish the datasets using BigQuery sharing listings for consumers to query via linked datasets, and configure Requester Pays on shared Cloud Storage buckets.: ### What the solution is This architecture combines **Dataplex Universal Catalog** for centralized metadata management and asset discovery with **BigQuery sharing (Analytics Hub)** listings and **Requester Pays** Cloud Storage buckets to enforce zero-copy data publishing and subscriber-funded compute. ### How it meets the requirements - **Discoverability & Metadata**: Dataplex Universal Catalog automatically indexes technical schemas for BigQuery tables and curated data lake filesets while allowing publishers to attach business glossaries, tags, and documentation URLs. - **Cost-Sharing Model**: In BigQuery sharing, the publisher pays solely for the persistent storage of the data. Subscribers pay for all compute resources consumed when running queries against their local linked datasets (using on-demand or capacity-based reservations). Similarly, enabling **Requester Pays** on Cloud Storage buckets ensures that API and egress network charges are billed to the requesting consumer project rather than the publisher. - **Eliminating Replication**: BigQuery sharing establishes zero-copy **linked datasets** in subscriber projects that serve as live read-only pointers to the source data, preventing data drift and redundant storage costs. ### Key advantages - **Zero-Copy Architecture**: Allows hundreds of external and internal consumers to access real-time data without pipeline orchestration or data synchronization. - **Granular Cost Attribution**: Shifts variable query compute and data extraction costs directly to consumer billing accounts. - **Centralized Governance**: Standardizes metadata tagging, searchability, and data access policies in place without data movement. - **Secure Publishing**: Preserves security controls such as row-level access and column-level security across linked datasets. ### Why this is optimal This approach aligns with Google Cloud data mesh best practices by separating data storage from analytical compute, providing robust discovery, and automating cost attribution across organizational boundaries.

Dataplex Catalog Analytics Hub Cost Sharing | professional-cloud-data-engineer

Dataplex Catalog Analytics Hub Cost Sharing

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A retail enterprise wants to establish a decentralized data-sharing architecture to publish curated demand-forecasting datasets and file assets to hundreds of vendor partners and internal domain teams. The data architecture team needs to implement a solution that fulfills the following operational and financial requirements:

Provide centralized discoverability and rich metadata annotations across all published SQL datasets and storage filesets.
Enforce a cost-sharing model where the publishing team pays only for underlying data storage, while consumers incur the compute and query execution costs.
Eliminate unnecessary data replication and maintenance overhead across consumer projects.

Which combination of Google Cloud services and configurations should the enterprise implement?

Catalog the assets using Dataplex Universal Catalog, publish the datasets using BigQuery sharing listings for consumers to query via linked datasets, and configure Requester Pays on shared Cloud Storage buckets.

Implement daily Dataflow batch pipelines to export and copy published tables and Cloud Storage filesets directly into each subscribing consumer project.

Publish datasets as BigLake external tables over Cloud Storage, granting consumers Storage Object Viewer access to the underlying bucket without configuring data exchanges.

Create authorized views in the publisher BigQuery project, provision a dedicated flat-rate slot reservation assigned to those views, and grant IAM Data Viewer roles directly to consumer service accounts.

Professional Cloud Data Engineer

Dataplex Catalog Analytics Hub Cost Sharing

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