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1. Hadoop Distributed File System (HDFS):Serving as the backbone of Hadoop, HDFS offers a scalable and fault-tolerant environment for storing vast amounts of data across multiple nodes, ensuring data availability even in the face of hardware failures.
2. MapReduce: This paradigmatic model facilitates the parallel processing of extensive datasets, empowering developers to craft programs capable of executing across clusters of commodity hardware, thus accelerating data analysis.
3. YARN (Yet Another Resource Negotiator): Functioning as a resource management system, YARN enables the orchestration of multiple data processing engines within a single Hadoop cluster, facilitating simultaneous workload execution.
4. Hadoop Common: Acting as a foundational layer, Hadoop Common provides a suite of utilities and APIs utilized across all Hadoop modules, streamlining cluster management and configuration tasks.
5. Hadoop Oozie: Oozie serves as a workflow scheduling system, empowering developers to define and manage intricate job workflows, ensuring seamless execution on the Hadoop cluster.
6. Hadoop Hive: Offering a data warehousing solution with a SQL-like query language, Hive simplifies the management and analysis of massive datasets stored within Hadoop, facilitating efficient data querying for analysts.
7. Hadoop Pig: As a high-level data-flow language, Pig enables the creation of data processing pipelines, simplifying the manipulation and transformation of large datasets within the Hadoop environment.
8. Hadoop HBase: Designed as a distributed NoSQL database, HBase operates atop HDFS, delivering real-time access to structured data stored within Hadoop, catering to diverse data management needs.

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