Describe the Hadoop ecosystem. What are its core components, and how do they work together?

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The Hadoop ecosystem is a collection of open-source tools designed to store and process large datasets across clusters of commodity hardware. While newer technologies have emerged, understanding Hadoop remains important because many organizations still run Hadoop-based infrastructure, and its concepts underpin modern big data architectures.

HDFS, the Hadoop Distributed File System, provides the storage layer. It splits large files into blocks, typically 128 MB each, and distributes them across cluster nodes. Each block is replicated, usually three times, across different nodes to ensure fault tolerance. The NameNode manages the filesystem metadata, tracking which blocks belong to which files and where they are stored. DataNodes store the actual blocks and serve read and write requests.

YARN, or Yet Another Resource Negotiator, provides the resource management layer. It allocates CPU and memory to applications running on the cluster. The ResourceManager is the central authority that arbitrates resources. NodeManagers run on each machine and manage containers where application tasks execute. YARN enables multiple processing frameworks to share the same cluster, not just MapReduce.

MapReduce is the original processing framework. It breaks computation into two phases. The map phase processes input data in parallel, producing intermediate key-value pairs. The reduce phase aggregates those pairs by key to produce final results. While effective for batch processing, MapReduce writes intermediate results to disk, making it slow for iterative computations.

Beyond the core components, the ecosystem includes several important tools. Hive provides a SQL-like interface for querying data stored in HDFS, making big data accessible to analysts familiar with SQL. Pig offers a high-level scripting language for data transformation pipelines. HBase is a NoSQL database built on HDFS for random read and write access. ZooKeeper handles distributed coordination and configuration management. Sqoop transfers data between Hadoop and relational databases. Flume and Kafka collect and ingest streaming data into the ecosystem.

The ecosystem evolved because no single tool handles every big data use case. Each component specializes in a specific function, and they work together through shared storage on HDFS and resource management through YARN.