Secure Enterprise Data Pipeline Optimization through Real-Time Event Processing and Cloud-Native Integration Frameworks
DOI:
https://doi.org/10.30750/Keywords:
Enterprise Data Pipelines, Real-Time Event Processing, Cloud-Native Integration, Data Security, Event-Driven Architecture, Stream Processing, Microservices, Cloud Computing, Data Governance, Pipeline Optimization, Cybersecurity, Distributed Systems.Abstract
The rapid growth of enterprise data has increased the demand for secure, scalable, and efficient data pipeline architectures capable of processing information in real time. Traditional batch-oriented data processing systems often experience latency, scalability limitations, and integration challenges when managing large volumes of heterogeneous data across distributed enterprise environments. This study examines secure enterprise data pipeline optimization through real-time event processing and cloud-native integration frameworks. It explores how event-driven architectures, stream-processing technologies, microservices, containerization, and cloud-based integration platforms can improve data processing performance while maintaining confidentiality, integrity, and availability. The study also investigates the role of encryption, identity and access management, continuous monitoring, data governance, and automated security controls in protecting enterprise information throughout its lifecycle. A research-oriented methodological framework is proposed to evaluate pipeline performance using latency, throughput, scalability, resource utilization, fault tolerance, and security effectiveness as key assessment parameters. The integration of real-time event processing with cloud-native infrastructure provides opportunities to reduce operational overhead, improve data accessibility, and support intelligent enterprise decision-making. The study highlights the importance of balancing computational efficiency with security requirements to establish resilient, flexible, and trustworthy enterprise data ecosystems. The proposed approach contributes to the development of optimized data engineering architectures for modern digital enterprises.
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