Architecting Enterprise Cloud Data Pipelines Through Distributed Event Streaming and Automated Integration Frameworks

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Published: 2023-12-20
DOI: https://doi.org/10.15662/IJRPETM.2023.0606031
Keywords:
Enterprise Cloud Data Pipelines, Distributed Event Streaming, Automated Integration Frameworks, Cloud Computing, Event-Driven Architecture, Apache Kafka, Real-Time Data Processing, Data Orchestration, Microservices, Data Integration, Scalability, Data Governance

Main Article Content

Dr.V.R.Vimal
Professor, Department of Computer Science & Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Abstract

Enterprise organizations increasingly depend on cloud-based data pipelines to process, integrate, and analyze large volumes of information generated by distributed applications, digital platforms, and interconnected business systems. Traditional batch-oriented integration approaches often struggle to meet modern requirements for scalability, low latency, data consistency, and operational flexibility. Distributed event streaming and automated integration frameworks provide an effective architectural foundation for developing responsive, reliable, and scalable enterprise data pipelines. This study examines the architectural principles, technological capabilities, and implementation strategies involved in integrating distributed event streaming platforms with automated cloud integration frameworks. It explores the role of event-driven architectures, microservices, message brokers, data orchestration, automated data transformation, and cloud-native infrastructure in improving enterprise data management. The research also investigates essential considerations, including fault tolerance, data governance, interoperability, security, observability, and resource optimization. Particular attention is given to how technologies such as Apache Kafka, Apache Flink, Kubernetes, and cloud-based integration services support real-time data processing and automated workflows. The study adopts a qualitative research methodology based on existing literature, architectural frameworks, and comparative technological analysis. The findings emphasize that combining distributed event streaming with automated integration mechanisms enables enterprises to establish adaptable data ecosystems, improve operational efficiency, and support informed decision-making in increasingly complex digital environments.

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Article Details

Issue

Vol. 6 No. 6 (2023): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Architecting Enterprise Cloud Data Pipelines Through Distributed Event Streaming and Automated Integration Frameworks. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9891-9899. https://doi.org/10.15662/IJRPETM.2023.0606031

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