👉 In the context of data processing and machine learning, columns computing refers to a technique used in distributed computing frameworks like Apache Spark to perform computations on large datasets across multiple nodes in a cluster. Each column in these computations represents a specific operation or transformation applied to the data, such as aggregations (e.g., sum, average), filtering, or transformations (e.g., converting data types). These operations are executed in parallel across the distributed nodes, enabling efficient and scalable processing of massive datasets. By breaking down complex computations into smaller, manageable tasks, columns computing facilitates the handling of big data challenges that would be infeasible with traditional single-node processing methods.
