Event-Driven Microservices for Real-Time Revenue Recognition in Cloud-Based Enterprise Applications
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Abstract
Live revenue recognition is an urgent demand in the current cloud based enterprise applications as businesses are ready to have real time financial information and regulatory compliance. The monolithic traditional architectures have been found not to be very compatible with providing scalability, responsiveness, and flexibility needed to handle dynamic transaction processing. This paper examines how event-driven microservices can be used to support real-time recognition of the revenue in cloud environments. Through asynchronous messaging, event sourcing and domain-driven design, the proposed architecture can decouple financial processes into scalable independence services that can process revenue events as they happen. An implementation prototype shows reduced latency, throughput, and systems resilience; this is a positive change over the traditional batch based designs. The results show that event-based microservices adoption does not only improve real-time financial processing but also auditability, compliance, and operational agility of enterprise applications. This study offers a practical solution to organizations that want to transform their financial systems to enable them have real-time revenue transparency in cloud-based systems.
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Pullamma, S. K. R. (2022). Event-Driven Microservices for Real-Time Revenue Recognition in Cloud-Based Enterprise Applications. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 14(04), 176-184. Retrieved from https://smsjournals.com/index.php/SAMRIDDHI/article/view/3417
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References
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[4] Olayinka, O. H. (2021). Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness. Int J Sci Res Arch, 4(1), 280-96.
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[6] Elger, P., & Shanaghy, E. (2020). AI as a Service: Serverless machine learning with AWS. Manning.
[7] Patel, U., Tanwar, S., & Nair, A. (2020). Performance analysis of video on-demand and live video streaming using cloud based services. Scalable Computing: Practice and Experience, 21(3), 479-496.
[8] Familiar, B., & Barnes, J. (2017). Business in Real-Time Using Azure IoT and Cortana Intelligence Suite. Apress: Berkeley, CA, USA.
[9] Mandala, V. (2016). Latency-Aware Cloud Pipelines: Rede ning Real-Time Data Integration with Elastic Engineering Models.
Global Research Development (GRD) ISSN: 2455-5703, 1(12).
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ACM Computing Surveys (CSUR), 54(9), 1-38.
[11] Joshua, Olatunde & Ovuchi, Blessing & Nkansah, Christopher & Akomolafe, Oluwabunmi & Adebayo, Ismail Akanmu &
Godson, Osagwu & Cli ord, Okotie. (2018). Optimizing Energy E ciency in Industrial Processes: A Multi-Disciplinary Approach to Reducing Consumption in Manufacturing and Petroleum Operations across West Africa.
[12] Nkansah, Christopher. (2021). Geomechanical Modeling and Wellbore Stability Analysis for Challenging Formations in the Tano Basin, Ghana.
[13] Adebayo, Ismail Akanmu. (2022). ASSESSMENT OF
PERFORMANCE OF FERROCENE NANOPARTICLE -HIBISCUS
CANNABINUS BIODIESEL ADMIXED FUEL BLENDED WITH
HYDROGEN IN DIRECT INJECTION (DI) ENGINE. Transactions of Tianjin University. 55. 10.5281/zenodo.16931428.
[14] Adebayo, I. A., Olagunju, O. J., Nkansah, C., Akomolafe, O., Godson, O., Blessing, O., & Cli ord, O. (2019). Water-EnergyFood Nexus in Sub-Saharan Africa: Engineering Solutions for Sustainable Resource Management in Densely Populated Regions of West Africa.
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[16] Kumar, K. (2022). How Institutional Herding Impacts Small Cap Liquidity. Well Testing Journal, 31(2), 97-117.
[17] Shaik, Kamal Mohammed Najeeb. (2022). Security Challenges and Solutions in SD-WAN Deployments. SAMRIDDHI A Journal of Physical Sciences Engineering and Technology. 14. 2022.
10.18090/samriddhi.v14i04..
[18] Adebayo, Ismail Akanmu. (2022). ASSESSMENT OF
PERFORMANCE OF FERROCENE NANOPARTICLE -HIBISCUS
CANNABINUS BIODIESEL ADMIXED FUEL BLENDED WITH
HYDROGEN IN DIRECT INJECTION (DI) ENGINE. Transactions of Tianjin University. 55. 10.5281/zenodo.16931428.
[19] SANUSI, B. O. (2022). Sustainable Stormwater Management: Evaluating the Effectiveness of Green Infrastructure in Midwestern Cities. Well Testing Journal, 31(2), 74-96.
[20] Olagunju, Joshua & Adebayo, Ismail Akanmu & Ovuchi, Blessing & Godson, Osagwu. (2022). Design Optimization of Small-Scale Hydro-Power Turbines for Remote Communities in Sub-Saharan Africa: A Nigerian Case Study.
[21] Kumar, K. (2022). Investor Overreaction in Microcap Earnings Announcements. International Journal of Humanities and Information Technology, 4(01-03), 11-30.
[22] Shaik, Kamal Mohammed Najeeb. (2022). MACHINE LEARNINGDRIVEN SDN SECURITY FOR CLOUD ENVIRONMENTS.
International Journal of Engineering and Technical Research (IJETR). 6. 10.5281/zenodo.15982992.
[23] Kumar, K. (2022). How Institutional Herding Impacts Small Cap Liquidity. Well Testing Journal, 31(2), 97-117.
[24] Buyya, R., Srirama, S. N., Casale, G., Calheiros, R., Simmhan, Y., Varghese, B., ... & Shen, H. (2018). A manifesto for future generation cloud computing: Research directions for the next decade. ACM computing surveys (CSUR), 51(5), 1-38.
[25] Stein, M. (2019). Adaptive event dispatching in serverless computing infrastructures. arXiv preprint arXiv:1901.03086.
[26] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram, Varun Teja Bathini. (2020). Secure Automation Frameworks for Smart Manufacturing Using Blockchain-Assisted Traceability.
International Journal of Research & Technology, 8(2), 47–53.
Retrieved from https://ijrt.org/j/article/view/879
[27] Dunie, R., Schulte, W. R., Cantara, M., & Kerremans, M. (2015).
Magic Quadrant for intelligent business process management suites. Gartner Inc.
[28] Zykov, S. V. (2018). Agile Services. In Managing Software Crisis: A Smart Way to Enterprise Agility (pp. 65-105). Cham: Springer International Publishing.
[29] Nalluri, S. K., & Parasaram, V. K. B. (2016). Early Approaches to Robotic Process Automation in Enterprise Systems. International Journal of Humanities and Information Technology, 1(01), 12-28.
https://doi.org/10.21590/ijhit.01.01.06
[30] Parasaram, V. K. B., & Nalluri, S. K. (2016). A Comparative Analysis of Risk Management Frameworks in Enterprise IT Projects. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 8(02), 147155. https://doi.org/10.18090/samriddhi.v8i2.7149
[31] Zhang, M. L. (2021). Intelligent Scheduling for IoT Applications at the Network Edge (Doctoral dissertation, University of California, Santa Barbara).