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Answer: 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.
Cloud Data Fusion is the recommended service because it meets all of TerramEarth's requirements. It is a fully managed service that allows for the quick and easy creation of data pipelines with minimal coding through its graphical interface. This aligns with the need for a fast, managed solution that requires little to no coding. Additionally, it is cost-effective and scalable, making it suitable for handling future data growth. - **Option A (Cloud Dataprep)** is incorrect as it is primarily focused on data preparation for analysis and machine learning, not for building data pipelines. - **Option C (Cloud Dataproc)** is incorrect because, while it is managed, it involves more complexity and is better suited for running big data frameworks like Apache Spark and Hadoop, not for codeless data pipeline creation. - **Option D (Cloud Dataflow)** is incorrect due to its requirement for coding to define data processing jobs, despite its capabilities in handling both stream and batch data processing.
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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.