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Now, let's explore how **Apache Spark** processes data step-by-step. The process begins with the driver program creating RDDs from your data source (like a file system, database, or cloud storage). These RDDs are then divided into partitions, which are smaller chunks of data that can be processed in parallel across the cluster. This parallel processing is what gives Spark its speed. Data is loaded into the cluster from various sources. The driver program then applies transformations to the RDDs. Transformations are operations that create new RDDs from existing ones (e.g., filtering data, mapping data, joining datasets). These transformations are *lazy*–meaning they are not executed immediately. Instead, Spark builds a directed acyclic graph (DAG) of transformations, which optimizes the execution plan.
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