GCP Professional Data Engineer Practice Question

A multinational retail bank must build a weekly ETL that extracts data from an on-premises Oracle database, a SaaS CRM, and Cloud Storage, then cleans, joins, and loads it into BigQuery. Data engineers and business analysts-many with limited coding skills-need to graphically design, version, and monitor the pipelines while the service handles execution and scaling without manual cluster management. Which Google Cloud service should they use as the central data-integration platform?

Dataform within BigQuery
Cloud Dataflow templates orchestrated with Workflows
Dataproc Serverless running Spark pipelines
Cloud Data Fusion

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Answer Description

Cloud Data Fusion is a fully managed, serverless data-integration service that offers a drag-and-drop visual designer, extensive prebuilt connectors (including Oracle, common SaaS apps, and Cloud Storage), built-in lineage, and pipeline version control. It automatically compiles the visual pipelines into Dataflow (or Dataproc) jobs that run on Google-managed infrastructure, eliminating cluster administration.

Dataflow templates provide several ready-made jobs, but crafting new or modified pipelines typically involves writing Apache Beam code and they lack a visual designer; they also rely on separate orchestration services for scheduling and monitoring. Dataform is optimized for SQL-only transformations that run inside BigQuery and cannot ingest from Oracle or SaaS sources. Dataproc Serverless removes cluster sizing but still requires you to author and maintain Spark jobs rather than offering a no-code interface. Therefore, Cloud Data Fusion best meets the bank's requirements.

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