The significance of distributed computing in processing large datasets lies in its ability to improve performance and efficiency. By distributing computing tasks across multiple interconnected systems, organizations can handle vast amounts of data more quickly than with a single machine. This parallel processing capability enhances scalability, allowing systems to grow with increased data volumes. Moreover, distributed computing aids in fault tolerance, as the failure of one node does not compromise the entire system. This architecture is particularly valuable in fields like big data analytics, where timely access to information is crucial for decision-making.