Category: Data processing

According to the IPO (Input, Processing, Output) cycle, every computing task involves a fundamental sequence of steps. While some operations involve data manipulation, others, like gathering raw data, are merely preliminary actions, not processing itself.

The term “spark” can refer to two very different things depending on the context, which can lead to confusion about its ingredients; for example, Apache Spark, a distributed computing system, processes data up to 100 times faster than Hadoop MapReduce in certain workloads. This guide addresses the two primary interpretations of “spark”—the AdvoCare energy drink and the Apache Spark analytics engine—detailing the distinct ingredients of each to provide a clear and complete answer.

Apache Spark, one of the most active projects managed by the Apache Software Foundation, was developed to be 10 to 100 times faster than its predecessor, Hadoop MapReduce. But what is Spark and what does it do? This distributed processing system is essential for handling large-scale data workloads efficiently.