Fault Tolerance Hadoop framework divides data into blocks and creates various copies of blocks on several machines in the cluster. So, when any device in the cluster fails, clients can still access their data from the other machine containing the exact copy of data blocks. High Availability In the HDFS environment, the data is duplicated by generating a copy of the blocks. So, whenever a user wants to obtain this data, or in case of an unfortunate situation, users can simply access their data from the other nodes because duplicate images of blocks are already present in the other nodes of the HDFS cluster. High Reliability HDFS splits the data into blocks, these blocks are stored by the Hadoop framework on nodes existing in the cluster. It saves data by generating a duplicate of every block current in the cluster. Hence presents a fault tolerance facility. By default, it creates 3 duplicates of each block containing information present in the nodes. Therefore, the data is promptly obtainable to the users. Hence the user does not face the difficulty of data loss. Therefore, HDFS is very reliable. Replication Replication resolves the problem of data loss in adverse conditions like device failure, crashing of nodes, etc. It manages the process of replication at frequent intervals of time. Thus, there is a low probability of a loss of user data. Scalability HDFS stocks the data on multiple nodes. So, in case of an increase in demand, it can scale the cluster.
