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Hadoop is an open-source distributed processing framework and storage space for large data applications in a scalable cluster of computer servers. This Hadoop is at the center of an ecosystem of big data technologies. This supports data science and advanced analytics initiatives that include machine learning, data mining, and deep learning.

There are four Primary Frameworks.

  1. Hadoop Distributed File System: This is the initial system of the Hadoop ecosystem, and it works on the data that resides in the local storage. It provides throughput access to application data and also removes network latency.
  2. MapReduce: Large-scale data processing is worked through this program module called MapReduce.
  3. Yet Another Resource Negotiator: This resource management program is responsible for managing compute resources in a cluster and is used to schedule user applications. So, the primary work of this system is planning and allocating resources in a Hadoop system.
  4. Hadoop Common: This process includes used and shared utilities by other Hadoop modules. The instructions run the operation, and database sets are dispatched to various nodes. A single node parallel to other processing nodes operates each. After the entire process, the datasets are combined into one to manage them in a better way.

Benefits of Hadoop

  • Since it is an open-source framework, it can run on commodity hardware and has a large ecosystem of tools. So, Hadoop is low in cost for storing and managing extensive data.
  • It allows the resilience of the system and fault tolerance. It means the operation is redirected to other nodes if any node is damaged. This is because if the data is stored in the cluster, it is replicated around various nodes within the system. This will help in stimulating the chances of software failure.
  • This also provides flexibility in storing the data, so it does not require pre-processing and keeping as much data as possible whenever we want.

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