Hadoop Distributed

Hadoop Distributed File System (HDFS) is a distributed file system designed to store and manage very large datasets across a distributed cluster of commodity hardware. It is a core component of the Apache Hadoop ecosystem and is well-suited for storing and processing big data.

Key characteristics of HDFS include:

1. Distributed Storage: HDFS breaks large files into smaller blocks (typically 128 MB or 256 MB in size) and distributes these blocks across multiple nodes in a Hadoop cluster. This distribution allows for parallel data storage and processing.

2. Fault Tolerance: HDFS is designed for high fault tolerance. It replicates each block multiple times (usually three) across different nodes in the cluster. If a node or block becomes unavailable due to hardware failures, HDFS can still retrieve the data from replicas on other nodes.

3. Scalability: HDFS is highly scalable and can handle massive amounts of data by adding more commodity hardware to the cluster. This scalability makes it suitable for storing petabytes or exabytes of data.

4. Data Integrity: HDFS ensures data integrity through checksums. When data is read from or written to HDFS, it is verified using checksums to detect and correct errors.

5. Data Locality: HDFS is designed to take advantage of data locality. When processing data, it attempts to run computations on the same node where the data is stored to minimize data transfer over the network.

6. Write Once, Read Many: HDFS is optimized for write-once, read-many workloads. Once data is written to HDFS, it is typically not updated. Instead, new data is appended, and older versions are retained.

7. Namespace and Metadata: HDFS maintains metadata about files and directories, which is stored in a separate server called the NameNode. The NameNode manages the namespace and keeps track of the structure of the file system, while the actual data is stored in DataNodes.

8. Block-Based Storage: Data is stored in fixed-size blocks, which simplifies data management and distribution across the cluster.