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TerramEarth, a leading manufacturer of heavy equipment for mining and agriculture, is looking to automate their data pipeline process to handle usage, administrative, and billing data collected during maintenance services at their service centers. The company prioritizes a solution that is fast, fully managed, and requires minimal to no coding to implement, while also being cost-effective and scalable to accommodate future data growth. Considering these requirements, which Google Cloud service would you recommend? (Choose one correct option)
A
Cloud Dataprep - A serverless data preparation service for exploring, cleaning, and preparing data for analysis and machine learning.
B
Cloud Data Fusion - A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines with a graphical interface and minimal coding.
C
Cloud Dataproc - A fast, easy-to-use, fully managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way.
D
Cloud Dataflow - A unified stream and batch data processing service that's serverless, fast, and cost-effective, with the ability to handle both real-time and historical data.