Data locality refers to the practice of storing and processing data close to where it is generated or used. This approach minimizes the distance data must travel, which can enhance performance and reduce latency. By keeping data local, systems can operate more efficiently, especially in environments with large datasets or high traffic. In distributed computing, data locality is crucial for optimizing resource usage. For example, in frameworks like Apache Hadoop or Apache Spark, tasks are scheduled to run on nodes that have the required data nearby. This strategy helps improve speed and reduces the load on network bandwidth, leading to faster processing times.