Hadoop was developed for enabling the organizations to perform colossal scale analytics processing across the huge unstructured data sets. This unstructured data can consist of millions or billions of files which need to be read. In order to ensure high processing performance and to keep the costs down, the applications and the data were chosen to be kept on the same physical hardware. By doing this, the data movement is eliminated, and thus, it allows local processing.
Though HDFS offers efficient management of data across the dispersed nodes, but the contemporary object storage is a superior alternative to HDFS. Let’s see how:
- The first reason is that object storage offers better data protection. While the HDFS uses the internal server-class storage, it makes three copies of the data as its data protection strategy. This is not very economical and one of the alternatives is to leverage an object-based storage system which provides protocol access to Amazon Simple Storage Service which is also supported by Hadoop in addition to HDFS.
- The second reason is that HDFS exposes the master node. HDFS possesses a series of slave nodes along with a master node, where the slave nodes process data in order to send results to the master node. But, the drawback of this arrangement is that if the master node fails, it becomes impossible to access the rest of the cluster, and HDFS provides very limited protection for the master node. But, this can be resolved to a considerable extent by using the object based storage.
- The final reason is that HDFS does not allow independent scaling. Just like any other architecture,Hadoop also has varying degrees of demand for storage and compute capacity. But with HDFS, storage capacity and compute power needs to scale in the lockstep, which means you cannot add a resource without the other. But, with object storage this limitation can be overcome.