Explanation:
Correct Option: C. Executing nightly ETL jobs to consolidate and analyze sales data from global retail outlets.
Batch data pipelines are optimally designed for processing large volumes of data at scheduled intervals, making them ideal for tasks like nightly ETL (Extract, Transform, Load) jobs. These tasks are cost-effective, comply with data governance policies by processing data during off-peak hours, and can scale to handle petabytes of data. They involve:
Why other options are not suitable:
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In the context of designing a data processing system for a multinational corporation, which scenario best illustrates a common use case for batch data pipelines, considering the need for cost-effectiveness, compliance with data governance policies, and scalability to handle petabytes of data? Choose one correct option.
A
Processing real-time transactions for an online gaming platform to ensure low-latency user experiences.
B
Streaming live video content across multiple regions with minimal delay.
C
Executing nightly ETL jobs to consolidate and analyze sales data from global retail outlets.
D
Monitoring and analyzing social media feeds in real-time to detect and respond to emerging trends.