hajusandmetöötlus

Hajusandmetöötlus, also known as distributed data processing, refers to the process of breaking down large datasets into smaller, manageable pieces that are then processed simultaneously across multiple computers or nodes. This approach is essential for handling the massive volumes of data generated by modern applications and systems. Instead of relying on a single, powerful machine, distributed data processing distributes the computational load, enabling faster and more efficient analysis.

The core principle involves partitioning data and distributing these partitions to different processing units. Each unit works on its assigned subset of the data independently. Once the individual processing is complete, the results from each unit are aggregated to produce the final output. This parallel processing significantly reduces the time required to process large datasets compared to traditional sequential methods.

Common applications of hajusametöötlus include big data analytics, machine learning model training, large-scale database management, and scientific simulations. Frameworks like Apache Hadoop and Apache Spark are widely used to implement distributed data processing, providing tools and infrastructure to manage data distribution, task scheduling, and fault tolerance. The ability to scale horizontally by adding more nodes makes hajusametöötlus a highly adaptable solution for evolving data processing needs.