Apache Spark: A Distributed Processing Architecture
Summary
This article explains how Apache Spark works as a distributed, in-memory processing engine for large-scale data workloads. It walks through the core architecture, including the driver, cluster manager, executors, RDDs, DataFrames, and DAG-based execution. It also compares Spark with Hadoop MapReduce and outlines common use cases such as ETL, analytics, machine learning, and streaming. The piece finishes with production deployment and monitoring guidance, emphasizing observability, resource tuning, and job health. The content targets data and platform teams that want to run Spark reliably in production.
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