Why do we need Spark for very complex data processing tasks?
💡 Model Answer
Apache Spark is a distributed computing framework designed to process large volumes of data quickly. It handles complex tasks such as iterative machine learning, graph processing, and real‑time analytics by keeping data in memory across a cluster, which reduces disk I/O compared to traditional MapReduce. Spark’s DAG scheduler optimizes execution plans, and its APIs (DataFrame, Dataset, RDD) provide high‑level abstractions for transformations and actions. It also supports multiple languages (Python, Scala, Java, R) and integrates with Hadoop, Hive, and Kafka, making it a versatile choice for big‑data workloads that require speed, flexibility, and scalability.
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