Batch Computation
Batch computation is a data processing paradigm where large volumes of data are collected, stored, and processed in discrete groups or batches at scheduled intervals, rather than in real-time. It is commonly used for tasks like ETL (Extract, Transform, Load), data analytics, report generation, and machine learning model training, where processing can tolerate latency. This approach contrasts with stream processing, which handles data continuously as it arrives.
Developers should learn batch computation for scenarios involving large-scale data processing that does not require immediate results, such as generating daily sales reports, processing log files overnight, or training machine learning models on historical datasets. It is cost-effective and efficient for workloads where data can be aggregated and processed in bulk, often using distributed systems like Apache Hadoop or Spark to handle petabytes of data across clusters.