Reinventing Data Pipelines Intelligent Multi Cloud Engineering for Seamless Interoperability and Integration Frameworks
DOI:
https://doi.org/10.15662/tj96hj86Keywords:
Multi-Cloud Computing, Data Pipelines, Data Integration, Interoperability, Cloud Engineering, Intelligent Automation, Microservices Architecture, Data Orchestration, Stream Processing, Data Governance, API Integration, Distributed SystemsAbstract
The increasing complexity of modern data ecosystems has driven the need for advanced data pipeline architectures capable of operating seamlessly across multiple cloud environments. This paper presents a novel approach to reinventing data pipelines through intelligent multi-cloud engineering, focusing on achieving seamless interoperability and robust integration frameworks. The proposed system leverages Artificial Intelligence (AI) and automation to orchestrate data workflows across heterogeneous cloud platforms, enabling efficient data movement, transformation, and synchronization. By incorporating cloud-agnostic design principles, API-driven integrations, and containerized microservices, the framework ensures flexibility, scalability, and vendor independence. Advanced techniques such as metadata-driven processing, real-time stream analytics, and adaptive workload management are utilized to optimize pipeline performance and reliability. Furthermore, the architecture integrates security mechanisms, data governance policies, and compliance standards to ensure safe and trustworthy data operations across distributed environments. Experimental evaluations demonstrate that the intelligent multi-cloud pipeline significantly reduces latency, enhances data consistency, and improves system resilience compared to traditional single-cloud approaches. This research contributes to the development of next-generation data engineering frameworks that support dynamic, scalable, and interoperable enterprise data ecosystems in an increasingly distributed digital landscape.
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