HDFS (Hadoop Distributed File System):

HDFS is a distributed file system that forms a core part of the Apache Hadoop ecosystem. It is designed to store and manage vast amounts of data across a cluster of commodity hardware. Here are some key aspects of HDFS:

1. Distributed Storage: HDFS divides large files into smaller blocks (typically 128MB or 256MB in size) and replicates these blocks across multiple nodes in a Hadoop cluster. This distribution and replication of data ensure fault tolerance and high availability.

2. Master-Slave Architecture: HDFS follows a master-slave architecture. The key components include the NameNode (master) and DataNodes (slaves). The NameNode manages the metadata and namespace of the file system, while DataNodes store the actual data blocks.

3. Data Reliability: HDFS provides fault tolerance by replicating data blocks. By default, it replicates each block three times, ensuring that if one replica or even an entire node fails, the data remains accessible.

4. Write-Once, Read-Many Model: HDFS is optimized for large-scale data processing. It is typically used for write-once, read-many workloads, making it suitable for batch processing, log storage, and data warehousing.

5. High Throughput: HDFS is optimized for high throughput, meaning it can efficiently handle large data reads and writes. It may not be ideal for low-latency operations or small random reads/writes.

6. Data Integrity: Data integrity is maintained through checksums, and any corrupted data blocks are automatically detected and replaced with healthy replicas.

7. Scalability: HDFS is highly scalable. You can expand the cluster by adding more DataNodes to accommodate growing data needs.