Correct Option: C. ETL (Extract, Transform, Load)

ETL is the most widely recognized approach for managing data transformations within a data pipeline, especially in scenarios requiring handling large volumes of data, minimizing latency, and ensuring data quality. The process involves:

1. Extract: Data is gathered from diverse sources such as databases, files, or APIs.
2. Transform: The collected data undergoes cleaning, filtering, and conversion to prepare it for analysis or storage, ensuring it meets business needs.
3. Load: The processed data is then stored in a designated system, such as a data warehouse, ready for analysis.

This method ensures data is efficiently and reliably processed, making it the best choice for the given scenario.

Why other options are less suitable:

• A. LET (Load, Extract, Transform): This is not a standard practice in data processing and does not efficiently handle the transformation of large volumes of data.
• B. ELT (Extract, Load, Transform): While valid, this approach can be less efficient for large datasets as transformation occurs after loading, potentially increasing latency.
• D. TLE (Transform, Load, Extract): This is also not a standard approach in data processing pipelines and may not ensure data is optimally prepared for analysis.