Designing Hybrid Cloud and Big Database Architectures for High Availability and Cost Efficiency
DOI:
https://doi.org/10.15662/IJRAI.2018.0102003Keywords:
Hybrid Cloud, Big Data Architecture, High Availability, Cost Efficiency, Distributed Databases, MongoDB, Cassandra, DataStax Enterprise, Hadoop, Cloud ComputingAbstract
Enterprises increasingly rely on data-intensive applications that demand high availability, fault tolerance, and predictable performance while operating under strict cost constraints driven by competitive markets and regulatory pressures. Traditional on-premise database infrastructures continue to offer strong control over data locality, security, and compliance requirements, yet they are often constrained by limited elasticity, long procurement cycles, and high capital expenditures that inhibit rapid scaling. In contrast, public cloud platforms enable near-instant provisioning, geographic distribution, and elastic resource utilization, but introduce concerns related to long-term operational costs, data sovereignty, and governance complexity when used exclusively. This article presents a hybrid cloud database and big database architecture that strategically integrates private and public cloud resources to balance these trade-offs, achieving high availability and cost efficiency simultaneously. By leveraging distributed NoSQL systems such as MongoDB, Apache Cassandra, and DataStax Enterprise alongside hybrid deployment patterns, the proposed architecture supports scalable analytics, resilient data storage, and workload-aware data placement across heterogeneous environments. Drawing on established research and practical architectures published between 2000 and 2017, this study synthesizes design principles, evaluates architectural and economic trade-offs, and proposes a reference model that guides enterprises in designing robust, flexible, and economically sustainable hybrid big database deployments.
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