GCP Professional Data Engineer Practice Question

A retail company needs to ingest daily incremental data from its on-premises Oracle database into BigQuery for analytics. The data engineers want to design and schedule the workflow in a drag-and-drop visual interface, avoid writing Spark or Beam code, and include cleansing transformations before the data is loaded. They prefer to reuse a JDBC connector after uploading the Oracle driver once. Which managed Google Cloud service best fits these requirements?

Cloud Dataprep powered by Trifacta
BigQuery Data Transfer Service
Dataproc with Spark and Workflow Templates
Cloud Data Fusion

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Answer Description

Cloud Data Fusion provides a fully managed, visual environment for building ETL and ELT pipelines without writing code. Engineers can upload an Oracle JDBC driver one time and then use the JDBC plugin to connect to the on-premises database. Data Fusion's UI lets them add transformation stages for data cleansing and schedule batch pipelines that load the processed data directly into BigQuery.

BigQuery Data Transfer Service offers a native Oracle connector but is focused on simple data movement and does not support arbitrary cleansing transformations or a visual pipeline designer. Dataproc requires engineers to manage clusters and write Spark jobs, while Cloud Dataprep is optimized for interactive, ad-hoc preparation rather than automated, end-to-end production pipelines.

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