How does Relativity effectively manage large volumes of data?

Relativity effectively manages large volumes of data by utilizing a highly scalable architecture. This means that the platform is designed to grow and adapt to the increasing amounts of data without compromising performance. Scalability is crucial in the realm of e-discovery and data management, where the size and complexity of datasets can vary significantly.

The architecture allows for efficient storage solutions and processing capabilities, making it possible for users to handle extensive datasets seamlessly. Users can add resources as needed to accommodate larger volumes of data or increased workloads, which is essential for projects that require handling substantial legal documents and case files.

This approach contrasts with singular data processing, which could bottleneck performance when faced with large datasets. Manual data entry processes would typically introduce inefficiencies and a higher risk of error. Relying solely on third-party software may not offer the integration and performance optimizations that come with a native scalable architecture developed specifically for managing large volumes of data in legal contexts. Therefore, the scalable architecture stands out as the most effective method for managing large datasets within Relativity.