An enterprise data platform spans multiple Google Cloud projects where distributed teams produce BigQuery analytical datasets and orchestrate machine learning pipelines in Vertex AI. The central governance team needs to implement a unified federated discovery and metadata management solution. The solution must meet the following requirements: Enable centralized search and discovery of data assets and machine learning pipeline artifacts across all projects Automatically visualize upstream root causes and downstream impacts through end-to-end lineage graphs Prevent naming collisions across distributed projects without centralizing the physical storage Which solution should you implement?

Integrate Vertex ML Metadata with Dataplex Universal Catalog, utilizing Fully Qualified Names (FQNs) for cataloged resources.: ### What Dataplex Universal Catalog and Vertex ML Metadata Integration is **Dataplex Universal Catalog** acts as a global, cross-project data fabric integrated with Google Cloud services like **Vertex AI**, **BigQuery**, and **Cloud Composer**. Combining it with **Vertex ML Metadata** creates a centralized catalog that tracks both analytical data assets and machine learning pipeline artifacts. ### How it meets the requirements - **Unified Discovery Across Distributed Sources**: **Dataplex Universal Catalog** allows cross-project search and metadata discovery without moving data out of individual project boundaries. - **End-to-End Lineage Tracking**: Lineage graphs composed of input, materialized, and output parameters/artifacts in **Vertex ML Metadata** sync directly to Dataplex, enabling root-cause and downstream impact analysis. - **Collision Prevention**: Dataplex assigns a **Fully Qualified Name (FQN)** to cataloged resources, ensuring unique identification across distributed domains. ### Key advantages - **Automated Lineage Graphs**: Visualizes transformations across multi-project data pipelines and ML workflows. - **Standardized Identification**: Uses **FQNs** to eliminate namespace conflicts across decentralized teams. - **Federated Governance**: Maintains decentralized data ownership while enabling centralized discovery and compliance auditing. - **Managed Scalability**: Requires no custom infrastructure or manual synchronization scripts. ### Why this is optimal This approach leverages native Google Cloud governance integrations to achieve global discoverability and automated lineage tracking while respecting distributed domain boundaries.

Dataplex Catalog Vertex Metadata Lineage Discovery | professional-cloud-data-engineer

Dataplex Catalog Vertex Metadata Lineage Discovery

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An enterprise data platform spans multiple Google Cloud projects where distributed teams produce BigQuery analytical datasets and orchestrate machine learning pipelines in Vertex AI. The central governance team needs to implement a unified federated discovery and metadata management solution.

The solution must meet the following requirements:

Enable centralized search and discovery of data assets and machine learning pipeline artifacts across all projects
Automatically visualize upstream root causes and downstream impacts through end-to-end lineage graphs
Prevent naming collisions across distributed projects without centralizing the physical storage

Which solution should you implement?

Import all distributed LookML project files into a central Looker project and use the LookML metadata panel to trace asset lineage.

Integrate Vertex ML Metadata with Dataplex Universal Catalog, utilizing Fully Qualified Names (FQNs) for cataloged resources.

Store all ML and BigQuery pipeline metadata exclusively in project-local Vertex ML Metadata stores and query them using Cloud Composer.

Configure Manufacturing Data Engine (MDE) metadata buckets and use the Metadata REST API to register analytical and ML artifacts.

Professional Cloud Data Engineer

Dataplex Catalog Vertex Metadata Lineage Discovery

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