Google Professional Machine Learning Engineer
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Your company is embarking on a project to create a unified analytics environment by integrating data from multiple on-premises data marts. However, the team is facing significant challenges with data quality and security during the data merging process. The project requires a cloud-native, cost-effective data integration solution that minimizes repetitive tasks and reduces overall costs. Additionally, to accommodate team members with varying technical skills, the solution should offer a codeless interface for developing ETL processes. Given these requirements, which Google Cloud service should you choose? (Choose one correct option)
Your company is embarking on a project to create a unified analytics environment by integrating data from multiple on-premises data marts. However, the team is facing significant challenges with data quality and security during the data merging process. The project requires a cloud-native, cost-effective data integration solution that minimizes repetitive tasks and reduces overall costs. Additionally, to accommodate team members with varying technical skills, the solution should offer a codeless interface for developing ETL processes. Given these requirements, which Google Cloud service should you choose? (Choose one correct option)
Explanation:
Cloud Data Fusion is the optimal choice for this scenario because it is a fully managed, cloud-native data integration service that provides a codeless interface for ETL process development. It addresses the need for minimizing repetitive tasks and reducing costs through automation, while also ensuring data quality and security across disparate data sources. This makes it ideal for creating a unified analytics environment without requiring extensive coding knowledge from all team members.