Question

You are an ML engineer joining a large enterprise with hundreds of BigQuery datasets spread across multiple projects owned by different business units. Before prototyping a churn model, you need to discover which existing tables contain customer transaction data, understand their schemas, check data quality metrics, and confirm you have appropriate access—without manually contacting each team. Which Google Cloud approach best enables this organization-wide data exploration and governance?

Accepted Answer

Use Dataplex to organize the datasets into lakes and zones, and use its integrated Data Catalog to search metadata, view schemas, and inspect data profiling and quality scans across projects.

Answer

Export every BigQuery table's schema to a shared Google Sheet and manually annotate which tables contain customer data for the team to reference.

Answer

Grant yourself BigQuery Admin on all projects, then run SELECT * queries against each table to inspect the data and infer its contents.

Answer

Provision a Vertex AI Feature Store and ingest all candidate tables so you can browse feature metadata in one place before modeling.