How to implement a data mesh for Cloud Storage and BigQuery data sharing?

Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments. What should you do?

1. Create a project for storage of the data for each of your departments.
1. Create multiple projects for storage of the data for each of your departments’ applications.
1. Create a project for storage of the data for your organization.
1. Create multiple projects for storage of the data for each of your departments’ applications. Source Reference Answer

Community Votes

84%

16%

84% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

This question tests knowledge of Google Cloud data mesh services; the trap is choosing Analytics Hub, which is simpler but primarily supports BigQuery, while Dataplex covers both BigQuery and Cloud Storage.

To implement a data mesh when sharing Cloud Storage and BigQuery data across departments on Google Cloud, the recommended approach is to create multiple projects for departmental applications and use Dataplex. Community consensus favors Dataplex over Analytics Hub because it supports both Cloud Storage and BigQuery assets.

The most common wrong answer is B, which uses Analytics Hub; it is a popular choice because it is simpler and directly enables data sharing, but it doesn't fully support Cloud Storage assets, making it insufficient for this scenario.

Community Discussion (11 comments)

raaad 👍 11 Selected: D

Decentralized ownership: Each department controls its data lake, aligning with the core principle of data ownership in a data mesh. - Self-service data access: Departments can create and manage their own Cloud Storage buckets and BigQuery datasets within their data lakes, enabling self-service data access. - Interdepartmental sharing: Dataplex facilitates data sharing by enabling departments to publish their data products from their data lakes, making it easily discoverable and usable by other departments.

daed09 👍 1

It says we want to work with GCS data assets, thus Dataplex is a better option, so it's option D! For those who says B as correct answer, I'd say that question states data assets in both GCS and BQ, however Analytics Hub is focused primarily for BQ assets.

plum21 👍 1 Selected: D

D because it looks like B is impossible due to the lack of GCS support in Analytics Hub

marlon.andrei 👍 1 Selected: B

In "a data mesh approach to share the data between sales, product design, and marketing departments", Analytics Hub is the solution.

Nandababy 👍 2

B is better option as organization is migrating to google cloud, that means teams doesnt have much hands on, analytical hub is more ease to use and solved the purpose as compared to dataplex were setup itself if very complex.

987af6b 👍 2 Selected: B

For a straightforward data mesh approach where the focus is on decentralizing data management while enabling easy data sharing and discovery, Analytics Hub is often the more appropriate choice due to its simplicity and directness. It facilitates the core objectives of a data mesh—decentralized data ownership and accessible data sharing—without the added complexity of managing data lakes and advanced governance features.

joao_01 👍 2

I think its B. I know since we are talking about Datamesh we want to go to the Dataplex service suddenly. However, in Dataplex a Lake can only have assets (bq tables etc) that are in the same project as the Dataplex service. Example: There is bq table in project A and B. I want to to create a Lake in Dataplex in Project A that contains tables of project B. I can´t do that, i can only host tables of the Project A, since the Lake is in project A. With this said, I think the best option is B, because the datamesh approach is related to "to share the data between sales, product design, and marketing departments". So the question is focusing only in the sharing part of the datamesh. Option B fits just fine.

JyoGCP 👍 1 Selected: D

Option D

Matt_108 👍 2 Selected: D

that's pure data mesh, which is what dataplex has been built for

Sofiia98 👍 1 Selected: D

For me, Dataplex looks more logical

GCP001 👍 1

D. Dataplex looks more suitable for data mesh approach, Check the ref - https://cloud.google.com/dataplex/docs/introduction

Comments & Corrections

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Expert Analysis

Why the Answer Is Correct

Dataplex is purpose-built for data mesh on Google Cloud. It supports managing data lakes across projects, domains, and zones, and its assets can be Cloud Storage buckets or BigQuery datasets. By creating multiple projects for each department's applications, you decentralize data ownership—a core data mesh principle—and then use Dataplex to share and govern data across departments. Comment [1] correctly emphasizes decentralized ownership and self-service data access, while comment [5] notes that this is "pure data mesh, which is what Dataplex has been built for."

Why the Other Options Are Wrong

Option B, which suggests using Analytics Hub, is often chosen because it is straightforward, but Analytics Hub focuses on sharing BigQuery datasets and lacks full support for Cloud Storage assets in this context—a critical flaw since the question explicitly includes Cloud Storage. Options A and C fail because they propose either one project per department or a single organization-wide project, which does not provide the multi-project isolation and decentralized ownership that a data mesh requires. Comment [7] highlights that B is "impossible due to the lack of GCS support in Analytics Hub," and comment [6] reinforces that the question's inclusion of GCS assets makes Dataplex the better choice.

Community Comment Notes

Most commenters correctly choose D. Comment [1] outlines how Dataplex aligns with data mesh principles: each department controls its data lake, enabling self-service access and interdepartmental sharing. Comment [6] and [7] point out that Analytics Hub is primarily for BigQuery and cannot handle Cloud Storage, which is why B is unsuitable. Comment [3] and [8] argue for B based on simplicity, but that argument overlooks the requirement to support both GCS and BigQuery. The debate centers on Dataplex vs Analytics Hub, and the weight of comments and official documentation strongly favors Dataplex.

Official Reference

Exam Strategy

Look for keywords like 'Cloud Storage' and 'BigQuery' in data mesh questions; if both are present, Dataplex is usually the right answer. Remember that Analytics Hub is more limited to BigQuery, so it will be selected for questions that only mention BigQuery data exchanges.

Related Analysis

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