DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Analyzing and processing a big data is a challenging task because of its various characteristics and presence of data in large amount. Due to the enormous data in today�s world, it is not only a challenge to store and manage the data, but to also analyze and retrieve the best result out of it. In this paper, a study is made on the different types available for big data analytics and assesses the advantages and drawbacks of each of these types based on various metrics such as scalability, availability, efficiency, fault tolerance, real-time processing, data size supported and iterative task support. The existing system approaches for range-partition queries are insufficient to quickly provide accurate results in big data. In this paper, various partitioning techniques on structured data are done. The challenge in existing system is, due to the proper partitioning technique, and so the system has to scan the overall data in order to provide the result for a query. Partitioning is performed; because it provides availability, maintenance and improvised query performance to the database users. A holistic study has been done on balanced range partition for the structured data on the hadoop ecosystem i.e. the HIVE and the impact on fast response which would eventually be taken as specification for testing its efficiency. So, in this paper a thorough survey on various topics for processing and analysis of vast structured datasets, and we have inferred that balanced partitioning through HIVE hadoop ecosystem would produce fast and an adequate result compared to the traditional databases.
