How does Relativity Web Processing efficiently handle large datasets?

Relativity Web Processing efficiently manages large datasets primarily through its use of a distributed architecture. This architectural approach allows the system to divide data processing tasks across multiple servers or nodes, enabling concurrent processing. As a result, large volumes of data can be handled more quickly and efficiently, as the workload is shared rather than being confined to a single machine.

In a distributed architecture, if one server is busy processing a part of the data, other servers can continue to work on different portions. This parallel processing capability significantly reduces the time required for data handling, making it well-suited for environments that demand rapid processing of large datasets—such as e-discovery in legal cases.

This contrasts sharply with options like single-threaded processing, which would bottleneck performance by queuing operations in a sequential manner, or manual data entry, which is time-consuming and prone to human error. Additionally, limiting file size does not address the overall efficiency of processing large datasets, as it may inhibit the comprehensive analysis needed in many cases. Using a distributed architecture is, therefore, the most efficient approach for handling large datasets in Relativity Web Processing.