Serverless Data Management Architectures for Multi Cloud Environments

In the ever-changing landscape of cloud computing, managing data across multi-cloud environments presents challenges and opportunities. This paper discusses serverless data management architectures optimized for multi-cloud deployments by demonstrating their feasibility and effectiveness using real-world datasets. We utilize publicly available data, such as AWS Public Datasets or Google Cloud BigQuery data, to run performance, scalability, and cost-effectiveness analyses of a set of serverless solutions. This is seen in the fact that serverless architecture will seamlessly integrate into a range of cloud platforms with ease, thus affording flexibility and reducing operational overhead. Further, we highlight key challenges regarding data consistency, latency, and security for multi-cloud environments. The findings indicate that the management of serverless data not only simplifies the complexity of multi-cloud operations but also provides a robust framework for businesses to scale their data-driven applications efficiently. This research helps further the understanding of best practices and strategic implementations that will help in the optimal management of multi-cloud data through serverless technologies.