Associate Professor, Gokaraju Rangaraju Institute of Engineering and Technology
Abstract
Our inability to handle huge quantities of data in a timely manner is one of the most important problems in the modern big data environment. In this article, using drive HQ cloud to compare and contrast 2 supervised multiplying systems based on service cluster implementations. On the other hand, Spark offers a more reliable data management framework as well as the ability to address issues including node loss and data duplication. Among other things, aquatic scientists research the flow and chemistry of water, aquatic species, aquatic ecosystems, the transport of items into and out of aquatic environments, and human usage of water. Aquatic scientists research both historical and contemporary processes, and the water bodies they study may be as large as whole oceans or as small as regions measured in millimetres.
Lalitha, Y. S. (2016). A Spark Implementation on Hadoop System for Big Data Analytics on Acquantic dataset. Int. J. of Aquatic Science, 7(2), 118-130.
MLA
Y. Sri Lalitha. "A Spark Implementation on Hadoop System for Big Data Analytics on Acquantic dataset". Int. J. of Aquatic Science, 7, 2, 2016, 118-130.
HARVARD
Lalitha, Y. S. (2016). 'A Spark Implementation on Hadoop System for Big Data Analytics on Acquantic dataset', Int. J. of Aquatic Science, 7(2), pp. 118-130.
VANCOUVER
Lalitha, Y. S. A Spark Implementation on Hadoop System for Big Data Analytics on Acquantic dataset. Int. J. of Aquatic Science, 2016; 7(2): 118-130.
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