Hadoop Distributed File System
HDFS is the acronym for Hadoop Distributed File System.
A distributed file system designed to store and manage large volumes of data across multiple nodes or machines. HDFS is a key component of the Apache Hadoop ecosystem, an open-source framework for distributed storage and processing of large datasets using the MapReduce programming model.
HDFS provides a scalable, fault-tolerant, and cost-effective solution for storing and processing Big Data. Some of its main features include:
- Distributed storage: HDFS stores data across a cluster of machines, automatically splitting large files into smaller blocks and distributing them across the nodes. This approach enables parallel processing and provides high throughput for data access.
- Replication: HDFS replicates each data block multiple times (by default, three replicas) across different nodes, ensuring data availability and fault tolerance in case of hardware failures.
- Scalability: HDFS can scale horizontally by adding more nodes to the cluster, allowing it to store and manage increasing amounts of data efficiently.
- High fault tolerance: The distributed nature of HDFS, along with its built-in data replication, makes it highly resistant to node failures, ensuring data integrity and system reliability.
- Data locality: HDFS aims to store data blocks on the same node or nearby nodes where the computation is taking place, minimizing data transfer costs and improving processing efficiency.
HDFS is widely used in Big Data applications, including data analytics, machine learning, and large-scale data processing tasks, in combination with other Hadoop ecosystem components like MapReduce, YARN, and Apache Spark.
- Abbreviation: HDFS