Big Data Architecture Framework for Data Analysis and Processing in Ecosystem
Big Data is a well-known buzzword in industry and academics, but there is still much conceptual uncertainty surrounding it, making it difficult to understand what it really means. The phrase is used to denote a wide range of concepts, from technology's ability to receive, repair, and use inform...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IJISAE
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11715/1/J16884_4087ed65d106a06f862584006d1e6318.pdf http://eprints.uthm.edu.my/11715/ |
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| Summary: | Big Data is a well-known buzzword in industry and academics, but there is still much conceptual uncertainty surrounding it, making it difficult to understand what it really means. The phrase is used to denote a wide range of concepts, from technology's ability to receive, repair, and use information to the cultural transformation that is extensively influencing both industries and societies, both of which are drowning in information. Research has gone in a lot of different areas because there is no official definition. This article explores the nature of big data, which can come from various fields of science, business, and social activity. It also suggests an improved big data definition that contains the following elements: Aspects of large data include facilities, safety, data infrastructure, data concepts, and data architectures. This article talks about the paradigm change in big data operations from traditional host- or service-oriented
design to data-centric architecture. The Big Data Architecture Framework (BDAF), which is proposed to handle every area of the Big Data Ecosystem, is made up of the following components. As a result, the analysis of big data is currently the subject of study and development. Examining the possible effects of big data challenges, unanswered research problems, and related tools is the primary objective of this work. Thus, this paper provides a paradigm for investigating big data at different stages. Academics now have novel
possibilities to develop the solution as a result of the difficulties and open research problems. |
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