Disk-Based Processing
Disk-based processing is a computing approach where data is stored and processed primarily on disk storage (like HDDs or SSDs) rather than in main memory (RAM). It is used when datasets are too large to fit entirely in memory, enabling the handling of big data by reading and writing data to disk in chunks. This method is common in database systems, data warehousing, and batch processing frameworks to manage large-scale data efficiently.
Developers should learn disk-based processing when working with large datasets that exceed available RAM, such as in big data analytics, ETL (Extract, Transform, Load) pipelines, or database management. It is essential for applications like data warehousing with tools like Apache Hadoop or database systems like PostgreSQL, where processing data in memory is not feasible due to size constraints, ensuring scalability and cost-effectiveness.