Hadoop Distributed File System (HDFS)

HDFS (Hadoop Distributed File System) is the primary storage system for Hadoop applications, designed specifically to run on commodity hardware and handle large datasets. It follows a master-slave architecture with a NameNode (master) that manages the file system metadata and DataNodes (slaves) that store the actual data. HDFS splits large files into blocks (typically 128MB or 256MB) and distributes these blocks across multiple DataNodes in the cluster. Each block is replicated multiple times (default replication factor is 3) to ensure fault tolerance and high availability. The NameNode maintains the file system namespace, tracking where file blocks are located across the cluster. It keeps track of which blocks make up a file, which DataNodes have those blocks, and the overall health of each DataNode. Key advantages of HDFS include: 1. Scalability: Can scale horizontally by adding more commodity servers to the cluster. 2. Fault Tolerance: Through data replication, HDFS ensures data remains accessible even if individual nodes fail. 3. High Throughput: Optimized for streaming data access patterns with high throughput rather than low latency. 4. Cost-effectiveness: Runs on commodity hardware rather than requiring expensive specialized storage systems. HDFS is optimized for batch processing rather than interactive use. It excels at "write-once, read-many" access patterns. While not ideal for low-latency data access or lots of small files, HDFS shines when processing massive datasets in parallel. Big Data Engineers use HDFS as the foundation for many data-intensive tasks, including ETL processes, data warehousing, and analytics workloads. HDFS forms the storage layer upon which other Hadoop ecosystem components like MapReduce, Hive, Spark, and HBase operate.

HDFS (Hadoop Distributed File System) is the primary storage system for Hadoop applications, designed specifically to run on commodity hardware and handle large datasets. It follows a master-slave ar…