Hadoop and Spark are two widely used distributed systems for managing large-scale data processing, each with its unique strengths and applications. Hadoop, originating from a Yahoo project in 2006, is well-suited for disk-heavy operations using the MapReduce paradigm, storing data across clusters via the Hadoop Distributed File System (HDFS). It includes components like YARN for resource scheduling and Mahout for machine learning. In contrast, Spark, developed in 2012 at UC Berkeley, offers a more flexible and faster in-memory processing architecture using Resilient Distributed Datasets (RDDs) and DataFrames, making it optimal for iterative tasks like machine learning. While Spark is generally more expensive due to its RAM requirements, it often runs in tandem with Hadoop, leveraging its fault tolerance and storage capabilities. Both systems are open-source Apache projects, and though they have distinct architectures, they are frequently used together to complement each other's capabilities, particularly when organizations require both batch and real-time analytics.