Jul 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 718-725· 0 citations
TL;DR
Using empirical benchmarks and advancements made in cloud computing technology and scheduling, this article investigates the architectural approaches, infrastructure optimization methods, and performance techniques that contribute to enabling high throughput and energy efficiency in transactional systems operating in real time.
Abstract
Real-time transaction processing has emerged as a foundational requirement of modern digital economies, underpinning payment networks, trading platforms, and fraud-detection systems that collectively handle trillions of dollars in daily transaction volume. As cloud-native architectures displace monolithic legacy systems, organizations face a dual imperative: delivering consistently sub-millisecond latency at global scale while simultaneously reducing the energy footprint of the infrastructure that sustains these workloads. Systems distributed across clouds using the architecture based on microservices, event-driven pipelines, containerization, and intelligent orchestration have been shown to be able to meet both requirements, although the engineering considerations that go into making this possible have not been extensively studied in the literature. This article investigates the architectural approaches, infrastructure optimization methods, and performance techniques that contribute to enabling high throughput and energy efficiency in transactional systems operating in real time. Using empirical benchmarks and advancements made in cloud computing technology and scheduling, the article further analyzes the social consequences of adopting such a system, including financial inclusion, sustainability, and trust.
This review examines the architectural, technological and organisational conditions required to develop scalable extract, transform and load pipelines for real-time transaction analytics. Its purpose is to clarify how contemporary data-engineering capabilities can be aligned with operational decision-making across transaction-intensive enterprises. The study adopts a structured narrative review of scholarly and technical literature on batch, micro-batch, stream-processing, Lambda, Kappa, event-driven, cloud-native, lakehouse and serverless architectures, with additional attention to governance, security, observability, resilience and emerging-economy implementation contexts.
The findings indicate that no single architectural model is universally optimal. Batch processing remains valuable for reconciliation, regulatory reporting and historical analysis, whereas stream-oriented and event-driven designs are better suited to fraud detection, payment monitoring, inventory visibility and other latency-sensitive operations. Hybrid architectures offer the strongest balance between speed, correctness, recoverability and cost. The review further finds that scalability depends not only on distributed computing, but also on partitioning, state management, change data capture, automated testing, lineage, data contracts, quality controls and service-level objectives. Organisational alignment, cross-functional ownership and regulatory compliance are equally decisive in determining whether technical capability produces measurable business value.
The study concludes that real-time analytical performance must be evaluated through both engineering and operational outcomes. It recommends use-case-driven architectural selection, resilient hybrid deployment, embedded security and governance, automated quality assurance, transparent AI-assisted pipeline management and stronger collaboration between technical and business teams. Future research should develop standardised benchmarks combining latency, accuracy, resilience, cost, sustainability and operational impact, while giving greater attention to infrastructure-constrained and emerging-economy environments. These priorities are essential for building data infrastructures capable of supporting responsive, evidence-based enterprise operations at scale.
Ogochukwu T Izuchukwu, Dominic Feboh, Ayokunle Olamide Ijagbemi et al.· International Journal of Mul...· 0 citations
Real-time enterprise computing is transitioning from periodic data movement and tightly coupled request-response integration onto continuous event streams, independently scalable services, and stateful processing that adapts as business conditions change. This review integrates research published between 2020 and 2025 on big data streaming, event-driven architectures, microservices, transactional stream processing, observability, and serverless execution. Its goal is to explain how these domains converge into a integrated architecture for next gen enterprise systems, rather than treating streaming as an isolated analytics component. A structured integrative review was carried out using DOI-verifiable, peer-reviewed sources from major computing publishers and journals. Thirty studies were retained based on their focus on architectural design, streaming semantics, scalability, reliability, consistency, deployment, or operational governance. Key factors such as event-time semantics, watermarks, durable logs, checkpointed state, idempotent consumption, schema evolution, distributed transaction models, and end-to-end observability collectively determine a system’s ability to deliver timely and trustworthy outcomes. Comparative evidence demonstrates that no stream-processing framework is universally optimal; workload characteristics, state requirements, deployment topology, and cost constraints considerably influence engineering decisions. While event-driven microservices enhance autonomy and extensibility, they also bring complexity in information consistency, asynchronous failure recovery, tracing, and governance. Serverless and edge patterns can reduce operational cost and network latency, but cold starts and distributed state management remain major challenges. This review proposes a layered enterprise event fabric and a correctness-resilience loop that integrate transport, stream computation, domain choreography, data governance, and operational assurance. The resulting research agenda highlights adaptive state management, portable semantics, energy-aware scheduling, automated data contracts, and verifiable real-time service-level objectives.
Adnan Anwar Shaikh· Global academic journal of e...· 0 citations
Aim: This study aimed to design and evaluate a cloud-native reference architecture for real-time payment processing that integrates microservices, event-driven communication, infrastructure-as-code, and federated AIOps to improve scalability, resilience, regulatory compliance, and operational efficiency. The novelty of the study is the federated three-tier integration model that combines ITIL-aligned governance, DevOps continuous delivery, and AIOps-driven anomaly detection within a single payment-specific reference architecture.
Methods: A mixed-methods design was employed, combining a structured synthesis of peer-reviewed and industry literature with quantitative benchmarking of instrumented cloud-native reference deployments. The deployments were scaled from 1 to 128 service replicas across two public clouds and an on-premise environment and were evaluated against a representative monolithic baseline.
Results: The findings show that the proposed cloud-native architecture reduced end-to-end p99 latency from approximately 850 ms in the monolithic baseline to approximately 180 ms, representing a 4.7× improvement. Sustained throughput scaled near-linearly to 78,000 transactions per second across 128 service replicas, compared with approximately 3,800 transactions per second in the baseline. Federated AIOps reduced Mean Time to Detect (MTTD) by 60% and Mean Time to Resolve (MTTR) by 50%, while alert volume and false-positive rates decreased by 70% and 82%, respectively. The architecture achieved 99.99% availability, with a Recovery Time Objective (RTO) of less than five minutes and a Recovery Point Objective (RPO) of less than 30 seconds across multi-region active-active deployments. The study also identifies operational complexity, multi-cloud cost overhead, and managed-service vendor lock-in as key limitations.
Conclusion: The findings indicate that cloud-native architectures can substantially improve the scalability, operational resilience, and regulatory readiness of real-time payment systems compared with conventional monolithic designs.
Recommendation: Financial institutions planning payment modernization should consider a phased adoption of cloud-native architectures supported by integrated governance, automation, and observability practices. The proposed framework should also be validated across diverse production payment environments.
Midhun Michael Nelavala· American Journal of Technolo...· 0 citations
Cloud-native architecture has been the new paradigm in enterprise application development that facilitates organization to reach unprecedented levels of scalability, agility, reliability, and operational efficiency. The dynamic business requirements, short delivery cycles of software solutions and the necessity of globally distributed services are mounting an increasing challenge on traditional monolithic systems. The concepts of microservices architecture, containerization, DevOps culture, and continuous delivery pipelines are the main principles in cloud-native systems that circumvent the limitations. This paper will be a detailed study of cloud-native architecture and its strategic role in a digital transformation process of businesses. Scalability models, distributed resource management, service orchestration, elasticity patterns and resilience techniques applied by cloud-native platforms are further detailed in the abstract. We address the transformation of enterprise application infrastructure out of on-premise legacy resource setting into service-based cloud environments properly configured to scale horizontally. The paper also investigates that the container orchestration systems such as Kubernetes make deployment, scaling, and failover operations to be declaratively automated. The purpose of service mesh, API-oriented architecture, event-based systems, policy-oriented autoscaling, and infrastructure-as-code (IaC) are examined to show how the architectural resilience and operational administration are accomplished. An approach to assess the maturity of cloud-native systems is presented based on the performance benchmarking, lifecycle automation, security compliance, and cost optimization indicators as part of a methodological framework. Also, the paper presents experimental evaluations of the response time, throughput, service resiliency, and infrastructure utilization in both traditional and cloud-native deployments. Findings indicate the application availability, frequency of deployment and scalability efficiency are very high. Lastly, such challenges as state management, data consistency, complexities in migrating, observability, and operational complexity are also addressed. The innovations that are emphasized by the best practices and future-oriented thinking include serverlesscomputing, auto-scaling that is run with AI, workloads based on WebAssembly, and automated cloud operations. All in all, the paper points to the idea that cloud-native architectures are not something the enterprises can afford to ignore on their quest towards maintaining competitive viability in the rapidly changing digital economy.
Kanya Mohammed, Naree Thongchai· International Journal of Mod...· 0 citations
Consumer streaming platforms face a structural challenge that conventional request-response web service designs cannot resolve: sustaining deterministic session and payment state during traffic surges that can exceed ten times the baseline load within a single minute. This paper presents an architecture engineered for enterprise-scale media streaming platforms to maintain system availability under these conditions. We introduce three coupled innovations: an asynchronous, Kafka-based event-driven pipeline that replaces synchronous execution chains and enforces exactly-once processing semantics; a deterministic geographic partitioning scheme using log-compacted materialized views to eliminate database-tier bottlenecks for over two million concurrent sessions; and an optimistic streaming revenue-protection model that decouples real-time playback access from external payment-gateway latency through a compensating-transaction framework. The architecture incorporates velocity-based predictive autoscaling, which forecasts traffic surges using edge login telemetry 60 to 90 seconds ahead of backend demand, alongside a zero-trust event mesh to secure distributed payloads. Empirical data from an industrial deployment demonstrates a 45% reduction in peak p99 latency (from 850 ms to 467 ms), sustained support for more than two million concurrent viewers, and a 25% reduction in off-peak infrastructure costs. Finally, this paper evaluates these outcomes against current literature in event-driven architecture, distributed state management, and cloud elasticity, arguing that these dimensions must be treated as a unified architectural requirement.
Nidhi Cheekireddy· Journal of Information Syste...· 0 citations
The exponential growth of data in the modern digital era necessitates efficient and scalable data processing mechanisms to extract meaningful insights in real time. Real-time analytics enables organizations to process, analyze, and visualize data streams instantaneously, providing critical insights that drive decision-making processes. However, designing scalable data pipelines for real-time analytics in big data systems presents several challenges, including data ingestion bottlenecks, efficient processing architectures, and ensuring low-latency responses. This paper explores the fundamental principles and methodologies involved in building scalable data pipelines, emphasizing architectural paradigms such as Lambda and Kappa architectures, and the role of distributed computing frameworks, stream processing engines, and cloud-based solutions. The paper further examines the impact of various data pipeline components, including data ingestion, processing, storage, and visualization, while discussing best practices for optimizing system performance, fault tolerance, and cost-effectiveness. A literature survey provides a comparative analysis of state-of-the-art real-time analytics frameworks and their scalability aspects. The methodology outlines the step-by-step design and implementation process of scalable data pipelines, supported by empirical evaluations. The results and discussions section presents performance benchmarks, evaluates latency metrics, and assesses the effectiveness of different data processing strategies. The paper concludes with recommendations for future research directions and potential improvements in scalable data pipeline design.
Lucas Martin· International Journal of Dat...· 1 citation