Managing Big Data With Hadoop MapReduce For Solving The Problems Of Traditional RDBMS

@article{1701904,
      author = {HTU RA},
      title = {Managing Big Data With Hadoop MapReduce For Solving The Problems Of Traditional RDBMS},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {8},
      pages = {89-94},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701904.pdf},
      abstract = {According to the usage of Internet is extremely increasing, the amount of data being generated by everywhere makes traditional database technologies unable to store and process efficiently and effectively. Moreover, Relational Database Management System (RDBMS) is hardly possible to manage extremely large amount of data called ?Big Data? in structured, semi-structured and unstructured forms from diverse data sources. In this paper, Hadoop, distributed big data processing platform, is applied for managing big data to overcome the storage and processing issues of traditional RDBMS. For experimentation, ?Bag of Words? dataset from UCI Machine Learning Repository is utilized as unstructured big text data tested on Apache Hadoop Map Reduce Multi Node Cluster. According to the outcomes of experimentation, applying Hadoop offers not only faster execution time but also better data scalability performance for managing and processing big text data.},
      keywords = {Bag of Words, Big Data, Hadoop, Map Reduce, RDBMS},
      month = {February},
  }