Which approach is used to process data in parallel for efficient analysis?

Distributed Computing is the correct approach for processing data in parallel to enable efficient analysis. This method involves splitting a large dataset across multiple machines or nodes within a network, allowing them to process different parts of the data simultaneously. By leveraging parallel processing, distributed computing can significantly reduce the time required to analyze large datasets, making it particularly effective for big data applications.

This approach supports scalability, as more computing resources can be added as needed to handle increasing volumes of data. It also enhances fault tolerance since if one node fails, others can continue processing, ensuring that the overall system remains operational.

In contrast, batch processing involves processing a large volume of data in predefined batches, often resulting in longer wait times before analysis can be completed. Stream processing is focused on real-time data processing and is more suited for continuous data flows rather than parallel analysis of large datasets. Centralized processing relies on a single location or system to handle all data processing tasks, which can create bottlenecks and inefficiencies if the data is extensive. Thus, distributed computing stands out for its ability to efficiently manage and analyze large datasets through parallel processing.