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Tuesday, July 24, 2018

5 Interesting Big Data Projects

Big data analytics has driven the last five years of machine learning. There are a lot of things that remain unexplored. One needs to have knowledge of frameworks such as the Hadoop Ecosystem to implement most big data projects. The MapReduce framework under Hadoop allows for massive scalability for distributed computing. Also, big data projects require immense processing power. There are two common ways one could get such processing power: Setting up a Server or Distributed Cluster for Parallel Computing: One can set up a machine which has multiple cores and high memory. Multiple cores are required for multiple threads to run in parallel. Memory requirements are generally high for big data. Another approach could be setting up multiple smaller machines and distribute workload amongst them (this is more scalable as more machines can be added if needed). However, such setups are expensive and need a lot of setting up. Cloud Computing: Companies such as Amazon, Google, and Microsoft have set up huge data centers for consumers.


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This article is related to

big data,big data analtics,big data use cases

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