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Apache Spark processes large-scale data across clusters. Spark SQL runs SQL queries on structured data. DataFrames organize data into named columns for analysis. RDDs provide low-level data manipulation with lineage. Transformations are lazy operations building execution plans. Actions trigger computation and return results. Spark Streaming processes real-time data with micro-batches. MLlib provides scalable machine learning algorithms. GraphX handles graph processing and analytics. Cluster manager options include standalone, YARN, and Kubernetes. Partitioning distributes data across cluster nodes. Caching keeps frequently accessed data in memory. Broadcast variables join operations. Accumulators aggregate data across partitions. Catalyst optimizer improves query execution performance. Tungsten engine optimizes memory and CPU usage. PySpark provides Python API for Spark. DataFrame API is recommended over RDDs for most cases. Spark is ideal for ETL pipelines and data processing. Understanding Spark's architecture helps optimize performance.