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Difference Between Cloud Computing and Hadoop

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  • Difficulty Level : Easy
  • Last Updated : 05 May, 2020

Building infrastructure for cloud computing accounts for almost one-third of all IT spending worldwide. Cloud computing is playing a major role in the IT sector, however, on the other hand, organizations started using Hadoop on a large scale nowadays for storing and performing actions on the increasing size of their data.


Cloud Computing:
Computing services such as storage, networking, databases, servers provided over the internet is known as Cloud computing. It is widely used because it saves the hardware costs for organizations, more secured with the latest technologies, less time taken for the sender and receiver communications i.e reduced network latency. Cloud data backup will be done by the cloud providers and can be accessed by users from anywhere using the Internet.
The three different cloud computing architecture are :

  • Public Cloud – operated by third-party cloud providers for example google cloud.
  • Private cloud – computing resources are used by the single organization for their own businesses needs.
  • Hybrid cloud – a combination of both public and private cloud features.

Hadoop is a software framework which allow users to process large data sets in a distributed environment. Depending upon the size of the data set computers are clustered in a distributed file system (DFS) manner. In Hadoop Distributed File System (HDFS) each file is divided into blocks of equal size, replicated thrice and stored randomly in Data Nodes. Many organizations started using Hadoop as their data warehouse since it can process data of different formats.

Below is a table of difference between Cloud Computing and Hadoop:

S.No. Cloud Computing Hadoop
1 Data is stored on cloud servers situated at different locations. Large data is processed and stored as volumes of data in a HDFS environment.
2 Constitutes complex computer concepts, involves large number of computers which are connected in real time. It is a framework with simple programming models to process data.
3 Data is stored and processed in remote servers up next accessed from any preferred location. The processed data yields new patterns hidden in the data.
4 Requires low maintenance, backup and recovery of data is available. Need more maintenance when compared and difficult to retrieve lost data.
5 Internet is used to provide cloud based services. Distributed computing is used for processing the data.
6 On demand services are provided by cloud platforms. Different formats of data is being processed and analysed.
7 Computing behaviour like Performance, scalability are analysed. Processed data will be analysed and stored.
8 No need to purchase expensive hardware . Business organizers can apply the predicted outcomes of the processed data in their businesses.
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