Abstract

Hybrid cloud architectures have emerged as a strategic approach for seamlessly integrating on-premises infrastructure with cloud-based big data solutions, offering flexibility, scalability, and cost-effectiveness. This paper investigates hybrid cloud architectures tailored for integrating on-premises and cloud-based big data solutions. We explore the key components and design principles of hybrid cloud architectures, including data integration, workload migration, and hybrid connectivity. Additionally, we discuss hybrid cloud management platforms and tools that facilitate orchestration, provisioning, and monitoring of hybrid environments. Through empirical evaluation and case studies, we assess the performance, scalability, and cost implications of different hybrid cloud architectures in real-world big data scenarios. Furthermore, we examine challenges, best practices, and emerging trends in deploying and managing hybrid cloud architectures for big data solutions. This study aims to provide insights and guidelines for practitioners and researchers in leveraging hybrid cloud architectures to achieve seamless integration and optimal utilization of on-premises and cloud-based resources for big data applications.

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