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:

• Extracting data from various sources (e.g., databases, files, APIs).
• Transforming the data into a format suitable for analysis or storage.
• Loading the processed data into a target system, such as a data warehouse or data lake.

Why other options are not suitable:

• A. Processing real-time transactions for an online gaming platform: Requires real-time data processing for low-latency interactions, not suited for batch processing.
• B. Streaming live video content across multiple regions: Demands real-time processing and delivery, not suited for batch processing.
• D. Monitoring and analyzing social media feeds in real-time: Needs immediate processing to detect and respond to trends as they happen, which is beyond the scope of batch processing.