Skip to main content
editor@theusajournals.com | Oscar Publishing Services Journal Home
Login

International Journal Of Management And Economics Fundamental

Peer Reviewed | Open Access | E-ISSN: 2771-2257
Published Article

From Data Streams To Decision Intelligence: Cloud-Native Data Warehousing Architectures

From Data Streams To Decision Intelligence: Cloud-Native Data Warehousing Architectures

Authors

  • Dr. Ibrahim Al-Hassan
    Department of Computer Science, University of Toronto, Canada

Keywords:

Cloud-native data warehousing real-time stream processing big data analytics

Abstract

The convergence of real-time data streams, cloud-native computing, and modern data warehousing has transformed how organizations derive value from data. Enterprises no longer rely solely on periodic batch processing and static analytical repositories; instead, they demand continuous ingestion, rapid transformation, and near-instant analytical insight to support operational and strategic decision-making. This shift has been driven by the proliferation of Internet of Things devices, digital platforms, cybersecurity monitoring, and data-intensive healthcare applications, all of which generate vast volumes of high-velocity, heterogeneous data. Within this evolving landscape, cloud-native data warehouses such as Amazon Redshift have emerged as central analytical backbones capable of integrating streaming and historical data while providing elastic scalability, high availability, and advanced analytical capabilities (Worlikar, Patel, & Challa, 2025). Yet despite the availability of sophisticated platforms, the theoretical and architectural foundations for integrating real-time stream processing, multiprocessor scheduling, and warehouse-centric analytics remain fragmented across disparate research traditions.

This article develops a comprehensive, publication-ready framework that unifies stream processing architectures, cloud-native data warehousing, and real-time scheduling theory into a coherent model for intelligent, large-scale analytics. Drawing on literature from big data stream analysis, distributed processing frameworks, real-time systems, cybersecurity, and healthcare analytics, the study argues that the performance and reliability of modern analytical systems are as dependent on scheduling and resource allocation as they are on data models and storage engines. Prior research has extensively examined individual components such as Apache Kafka, Spark, Storm, and Flink, as well as real-time scheduling algorithms for multiprocessor systems, yet few works have connected these layers to the warehouse-centric analytics that organizations ultimately depend on for decision-making (Kolajo, Daramola, & Adebiyi, 2019; Babcock et al., 2004; Anderson & Devi, 2006).

Methodologically, the study adopts a qualitative, theory-driven synthesis of the provided references, interpreting their empirical and conceptual contributions through the lens of cloud-native data warehousing. By positioning Amazon Redshift as an analytical anchor that interacts dynamically with streaming pipelines and scheduling frameworks, the article demonstrates how real-time business intelligence, cybersecurity monitoring, and healthcare analytics can be supported in a unified architectural paradigm (Delen et al., 2018; Alam et al., 2024; Buczak & Guven, 2016). The results reveal that performance, fairness, and quality of service in modern data warehouses are emergent properties of distributed scheduling, stream processing semantics, and storage-compute decoupling rather than isolated platform features.

The discussion extends these findings by engaging with competing scholarly perspectives on scalability, latency, and reliability in distributed analytics. It argues that future research must transcend platform-specific benchmarking and instead develop theoretically grounded models that integrate real-time scheduling, data stream management, and cloud-native warehousing. In doing so, the article contributes a rigorous, interdisciplinary foundation for the next generation of intelligent, real-time data warehouses capable of supporting mission-critical decision-making in complex digital ecosystems.

References

Gurusamy, V., Kannan, S., & Nandhini, K. (2017). The real time big data processing framework advantages and limitations. International Journal of Computer Sciences and Engineering, 5(12), 305–312.

Worlikar, S., Patel, H., & Challa, A. (2025). Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions. Packt Publishing Ltd.

Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.

Kleppmann, M., & Kreps, J. (2015). Kafka, Samza and the Unix philosophy of distributed data. IEEE Data Engineering Bulletin, 38(4), 4–14.

Alam, M. A., Sohel, A., Uddin, M. M., & Siddiki, A. (2024). Big data and chronic disease management through patient monitoring and treatment with data analytics. Academic Journal on Artificial Intelligence, Machine Learning, Data Science and Management Information Systems, 1(01), 77–94.

Baruah, S., Cohen, N. K., Plaxton, C. G., & Varvel, D. A. (1996). Proportionate progress: A notion of fairness in resource allocation. Algorithmica, 15(6), 600–625.

Amakobe, M. (2016). A comparison between Apache Samza and Storm. Colorado Tech University.

Henning, S., & Hasselbring, W. (2024). Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud. Journal of Systems and Software, 208.

Behera, R. K., Das, S., Jena, M., Rath, S. K., & Sahoo, B. (2017). A comparative study of distributed tools for analyzing streaming data. International Conference on Information Technology, 79–84.

Nasiri, H., Nahesi, S., & Goudarzi, M. (2019). Evaluation of distributed stream processing frameworks for IoT applications in smart cities. Journal of Big Data, 6.

Aldarwbi, M. Y., Lashkari, A. H., & Ghorbani, A. A. (2022). The sound of intrusion: A novel network intrusion detection system. Computers and Electrical Engineering, 104, 108455.

Kolajo, T., Daramola, O., & Adebiyi, A. (2019). Big data stream analysis: A systematic literature review. Journal of Big Data, 6.

Delen, D., Moscato, G., et al. (2018). The impact of real-time business intelligence and advanced analytics on the behavior of business decision-makers. International Conference on Information Management and Processing.

Katsifodimos, A., & Schelter, S. (2016). Apache Flink: Stream analytics at scale. IEEE International Conference on Cloud Engineering Workshop, 193.

Anderson, J. H., & Devi, U. C. (2006). Soft real-time scheduling on multiprocessors.

Block, A., Brandenburg, B. B., Anderson, J. H., & Quint, S. (2008). An adaptive framework for multiprocessor real-time systems. Euromicro Conference on Real-Time Systems, 23–33.

Babcock, B., Babu, S., Datar, M., Motwani, R., & Thomas, D. (2004). Operator scheduling in data stream systems. The VLDB Journal, 13(4), 333–353.