The main difference between Hadoop and HDFS is that the Hadoop is an open source framework that helps to store, process and analyze a large volume of data while the HDFS is the distributed file system of Hadoop that provides high throughput access to application data.
Big data refers to a collection of a large amount of data. It has major three properties: volume, velocity, and variety. It is not possible to use traditional DBMS to store this kind of massive data. Hadoop is an alternative to this issue. It is an open source framework written in Java that allows to store and manage big data effectively and efficiently. The distributed file system of Hadoop is HDFS. It is a module in Hadoop architecture.
Key Areas Covered
Big Data, DBMS, Hadoop, HDFS, Java
What is Hadoop
Hadoop is an open source framework developed by Apache Software Foundation. It helps to store and process big data simultaneously using simple programming models in a distributed environment. It also supports distributed storage and computation across clusters of computers. Organizations such as Facebook, Google, Yahoo, LinkedIn, and Twitter use Hadoop.
Hadoop provides a number of advantages. It is possible to extend a cluster by adding nodes to that cluster. Thus, it provides scalability. It is also possible to add and remove servers from the cluster dynamically. Moreover, Hadoop is cost effective as it is open source and use commodity hardware to store data. As Hadoop is written in Java, it is compatible on various platforms. Furthermore, Hadoop library allows detecting and handling faults at the application layer.
What is HDFS
There are multiple modules in Hadoop architecture. One of them is Hadoop Distributed File System (HDFS). It is the distributed file system of Hadoop. It distributes data over several machines and replicates them. Thus, improving fault tolerance and increases data availability.
There are blocks in HDFS. A block is a minimum amount of data that can be read or write. HDFS divides files into blocks. The master node or the name node handles the metadata of all the files in HDFS. The other nodes are slave nodes or data nodes. They store and retrieve blocks according to the master node’s instructions. Therefore, HDFS operates according to the master-slave architecture. Overall, HDFS makes Hadoop work faster. It also replicates data over the network to have minimum effect during a failure.
Difference Between Hadoop and HDFS
Hadoop is a collection of open source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. In contrast, HDFS is a Distributed File System that reliably stores large files across machines in a large cluster. Thus, this is the main difference between Hadoop and HDFS.
Hadoop helps to manage data storing and processing of a large set of data running in clustered systems while HDFS provides high-performance access to data across Hadoop clusters. Hence, this is another difference between Hadoop and HDFS.
The main difference between Hadoop and HDFS is that the Hadoop is an open source framework that helps to store, process and analyze a large volume of data while the HDFS is the distributed file system of Hadoop that provides high throughput access to application data. In brief, HDFS is a module in Hadoop.