Exam Professional Data Engineer topic 1 question 293 discussion

- 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.

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.

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.

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.

Option D

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

For me, Dataplex looks more logical

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