BIG DATA ANALYSIS: FOUNDATIONS, TECHNIQUES, AND RESOURCES
DOI:
https://doi.org/10.65009/jhqsh213Keywords:
—Big Data, Big data environment, Velocity, Traditional Data, Sources, Hadoop, Enterprise electronic documents, HDFS.,,Abstract
The revolution in technology is, in many respects, contributing to the rapid increase in the
amount of data that is being generated and captured simultaneously. As a consequence of this, the term
"big data" is today the most widely used keyword in each and every research and engineering field. Small
data is comparable to big data, with the exception that the volume of big data is substantially higher. The
term "big data" refers to information that is exceeding a certain threshold in terms of either petabytes or
terabytes in size. While it is realistic to anticipate that technological advancements will continue, it is also
reasonable to anticipate that the size of data sets, which will be referred to as Big Data, will also continue
to rise. On a daily basis, the amount of data is increasing at an exponential rate, and as a consequence, the
amount of data that is currently available has beyond the capacity of traditional databases to store it. Big
Data platforms, such as Hadoop, make it possible to manage and analyze massive datasets that are not
compatible with traditional databases. This is made possible by the way Hadoop works. This study's
objective is to investigate the introduction, characteristics, and multiple instruments that are utilized in the
process of managing and analyzing massive amounts of data. The storage, processing, and analysis of
enormous volumes of data has become a new challenge as a result of the rapid rise of data that has
occurred as a result of the development of social networks and cloud computing. The development of a
big data platform is required since traditional technologies are no longer enough for the processing of
massive amounts of data. It is indisputable that big data platforms provide users with assistance in the
development of analytical services in an effective manner. Nevertheless, the procedure of data collection,
the creation of algorithms, and the provision of analytics services all require time.
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