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As the lead for creating a unified analytics environment across various on-premises data marts, you're facing challenges with data quality and security due to the integration of data across servers. This issue stems from the use of disparate tools and temporary solutions. Your goal is to implement a fully managed, cloud-native data integration service that minimizes costs and reduces repetitive tasks. Additionally, your team prefers a codeless interface for developing ETL processes. Which service would best meet these requirements? Choose the best option from the following:
A
Dataflow: A fully managed service for stream and batch processing, offering a unified programming model but requires coding knowledge for ETL processes.
B
Apache Flink: An open-source stream processing framework that requires significant setup and maintenance, not fully managed or codeless.
C
Dataprep: A cloud-based service for data preparation and cleaning, but lacks comprehensive ETL capabilities and is not fully managed for data integration.
D
Cloud Data Fusion: A fully managed, cloud-native data integration service that supports a codeless interface for building ETL processes, ideal for reducing repetitive work and lowering costs.
E
None of the above: If you believe none of the options fully meet the requirements, select this option.