Big data architecture evolution

Mohammad, Atif Farid; Mcheick, Hamid; Grant, Emanuel S.

doi:10.1145/2656346.2656358

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…In some organisations, the scope of data architecture has expanded from determining the collection, storage and organisation of data to big data architecture, which involves design for large-scale big data solutions (Miller 2014). Mohammad, Mcheick and Grant (2014) claim that the scope of data architecture is not only limited to design but also to identifying and planning for communication with external and internal stakeholders. The above seems clear enough.…”

Section: Data Architecturementioning

confidence: 99%

The architectures of data and information: Their confounded confusion

Iyamu¹

2019

SA Journal of Information Management

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Background: Even though it is well documented in the academic domain, including various English dictionaries, that data and information are not the same thing, with the latter being a refinement of the former, many people within organisations and academic environments continue to loosely and interchangeably use these terms. This is a challenge that extends to the enterprise architecture discipline, which has an impact on how the domains of data and information architectures are defined, developed and implemented in many organisations. Objectives:The challenge leads to misunderstanding of both technical and business requirements, as well as confusion about the differentiation between data and information architectures. This challenge affects the accomplishment of either data or information in an organisation. This is caused by the fact that data is referred to as information, meaning it has been refined, which is incorrect, and therefore affects requirements. Additionally, this challenge influences the organisational structure and the development of employees' career. Thus, the objective of this study was to examine the implication of practice in the loose and interchangeable use of the terms, 'data' and 'information', within an organisation.Method: Based on this objective, the interpretivist stance was employed in the study. Qualitative data was gathered and the hermeneutics approach was employed in the analysis.Results: From the analysis, a solution is proposed to halt further misconstruction of the architectures, and avoid the confusing challenges it has for organisations. Conclusion:The study reveals what we need to know about the confounded confusion between data and information architectures. The differentiation between the two concepts can be of importance to professionals in the field of information systems and technologies as well as academics.

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Section: Data Architecturementioning

confidence: 99%

The architectures of data and information: Their confounded confusion

Iyamu¹

2019

SA Journal of Information Management

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“…More details about big data analytics in the real time were given by Barlow and Smith [1,24]. A mention of big data challenges in IT departments is important in this project, in addition to guidance to IT members with essential roles [13,25]. In terms of optimizing data storage method in large companies, Schlumberger, the oil and gas Exploration Company, and CATIA V5 Deployments have chosen their own data storage method to be performed by NetApp storage solutions to handle large quantities of data [7,9].…”

Section: Related Workmentioning

confidence: 99%

The Future of Data Storage: A Case Study with the Saudi Company

Ghamdi¹

2018

JEEE

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The age of big data has emerged. These data are generated from online transactions, emails, posts, videos, search queries, etc. People also produce data by using the Internet of Things (IoT) applications and devices. Storing these massive quantities of data has become one of the most important and critical issues for big companies like Google, LinkedIn, Yahoo, and for the digital society in general. Traditional data storage methods such as Relational Database Management Systems (RDBMSs) are coming under increase pressure due to their capability limitations. However, many of new technical solutions have proved their efficiency in storing big data for large companies; some examples of these solutions include NetApp, Hadoop, SAN, the cloud, data centres, etc. The storage, accessibility, and security of big data issues are not only a computer science concern; it has become a topic of interest in many fields such as healthcare, E-commerce, and business in general. This project is investigating the data storage methods and future requirements for one of the largest oil companies in the world, Saudi SA Oil Company. A survey was carried out to understand current storage problems, and collect requirements to implement effective storage methodologies that overcome most of SA's storage difficulties.

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“…In fact, big data has widely sources, including telecommunication, web sites, sales, marketing, RFID enabled logistics, utility of water and energy consumption, finance and insurance, health and prescription, environment, transport, biological, archaeological, geological exploration, nuclear physics, molecular biology, astronomy, and etc. [2]. Big data implies technology of platform, tools, techniques to capture, process, ingest, and visualize large datasets at a reasonable time and cost.…”

Section: Related Workmentioning

confidence: 99%

“…Big data system is comprised of big data ingestion, analysis, visualization, deployment and distribution modules [2]. Big data architecture of application system contains design, development, maintenance, decommissioning and integration.…”

Section: Big Data Architecturementioning

confidence: 99%

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Big Data Architecture, Platform, Application and Trend

Zeng¹

2016

dtetr

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Abstract. Big data has become the hottest topic with the cloud computing and massive data generation. Based on Hadoop, MapReduce and their ecosystem, there are number of improved and optimized big data dealing and analysis platforms. Extensive applications in the Internet of Things, social network, transport, medical, education, and etc. have attracted huge interests and attentions of researchers and industries. To form a hostile profile view, it is necessary to explore big data architecture, platform, application and trend. Abstract architecture and workflow graph of big data is presented, some hot platforms are reviewed and illustrated, and big data application and trend are proposed. The main contribution is to provide detailed and systematic survey and discussion to form a whole technology framework and overview of big data.

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Big data architecture evolution

Cited by 10 publications

References 8 publications

The architectures of data and information: Their confounded confusion

The architectures of data and information: Their confounded confusion

The Future of Data Storage: A Case Study with the Saudi Company

Big Data Architecture, Platform, Application and Trend

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