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Paper Type
Complete
Description
The proliferation of digital devices, rapid development of software and the infrastructure of today, have augmented user’s capability to produce data at an unprecedented rate. The accelerated growth of data could be called the era of big data and forced a paradigm shift in data engineering because the variety, velocity and volume of data overwhelmed existing systems. While companies attempt to extract benefit from big data, success rates are still low. Challenges such as rapid changes in technology, organizational culture, complexity in data engineering, impediments to system development, and a lack of effective big data architectures mean that only an estimated 20% of companies achieved their goals. To this end, this study explores a domain-driven distributed big data reference architecture that addresses issues in data architecture, data engineering, and system development. This reference architecture is empirically grounded and evaluated through deployment in a real-world scenario as an instantiated prototype, solving a problem in practice. The results of the evaluation demonstrate utility and applicability but with architectural trade-offs and challenges.
Paper Number
1410
Recommended Citation
Ataei, Pouya and Litchfield, Alan, "Towards a domain-driven distributed reference architecture for big data systems" (2023). AMCIS 2023 Proceedings. 11.
https://aisel.aisnet.org/amcis2023/sig_odis/sig_odis/11
Towards a domain-driven distributed reference architecture for big data systems
The proliferation of digital devices, rapid development of software and the infrastructure of today, have augmented user’s capability to produce data at an unprecedented rate. The accelerated growth of data could be called the era of big data and forced a paradigm shift in data engineering because the variety, velocity and volume of data overwhelmed existing systems. While companies attempt to extract benefit from big data, success rates are still low. Challenges such as rapid changes in technology, organizational culture, complexity in data engineering, impediments to system development, and a lack of effective big data architectures mean that only an estimated 20% of companies achieved their goals. To this end, this study explores a domain-driven distributed big data reference architecture that addresses issues in data architecture, data engineering, and system development. This reference architecture is empirically grounded and evaluated through deployment in a real-world scenario as an instantiated prototype, solving a problem in practice. The results of the evaluation demonstrate utility and applicability but with architectural trade-offs and challenges.
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