Apache Spark Handles Data Larger Than RAM With Distributed Processing

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Apache Spark is a distributed data processing engine designed for workloads where single-server RAM becomes a bottleneck. It was developed at Berkeley's AMPLab to overcome Hadoop MapReduce limitations by processing data in memory, making it significantly faster for iterative tasks like machine learning. The core concept is the Resilient Distributed Dataset (RDD), an immutable collection partitioned across cluster nodes that can rebuild itself if a node fails. Spark Structured Streaming provides micro-batch processing with stateful exactly-once semantics for real-time data. The same code can run identically on a local machine or a large cloud cluster, with Spark managing distribution and fault tolerance.

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