How Does Apache Spark Handle Large Datasets in Nanotechnology?
Spark's architecture is designed to manage large datasets efficiently. It divides data into smaller chunks called partitions, which are then processed in parallel across multiple nodes. This distributed approach not only accelerates data processing but also enhances fault tolerance, ensuring that the analysis continues smoothly even if some nodes fail. This is particularly useful in nanotechnology where datasets can be extraordinarily large and complex.
