Definition of HDFS

HDFS: HDFS (Hadoop Distributed File System) is a distributed file system that enables high throughput access to data across large clusters of commodity servers. HDFS is designed to scale to support very large data sets up to petabytes in size.

What is a HDFS used for?

HDFS, or Hadoop Distributed File System, is a distributed file system that works in parallel with multiple servers to store and process large amounts of data. It is used in data science and machine learning applications to store and access large datasets quickly. HDFS is designed so that it can be scaled up easily to accommodate larger amounts of data without any performance issues or latency. It also supports high-availability so that if one server fails, the other servers will make sure the system still remains operational.

HDFS is more fault-tolerant than traditional file systems as it utilizes redundancy by replicating blocks across multiple nodes. When a file is written to HDFS, the system first divides it into several blocks that are then written to different datanodes. If a node fails, other nodes will take over the failed node’s workload without the user having to manually intervene. Additionally, HDFS provides an easy way to access files stored on remote servers since they are divided into blocks and replicated across many nodes.

In terms of security, HDFS offers encryption for both data at rest and in transit using Kerberos authentication which makes it ideal for use in highly secure environments. Furthermore, due to its ability to run on commodity hardware and its scalability, HDFS has become widely popular among businesses looking for an efficient solution for managing Big Data workloads from massive amounts of users simultaneously.

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