Multi Cloud and Federated Data Management Frameworks

Abstract

Organizations are rapidly implementing multi-cloud strategies to take advantage of various cloud service companies for improved flexibility, effectiveness, and stability in an era of dispersed computation and accelerating expansion of data. This chapter examines how federated data administration frameworks, 167 Multi Cloud and Federated Data Management Frameworks which facilitate smooth data processing and transfer across several systems without centralizing data storage, interact with several cloud settings. We start by outlining multi-cloud architectures and their essential elements, such as connectors for periphery & hybrid computation. After that, the topic of federated data management is covered, with a focus on collaboration, data sovereignty, including preserving privacy. The functions of important technologies like Apache Arrow, Kubernetes Federation, and new standards from groups notably the Cloud Native Computing Foundation (CNCF) in coordinating processing of data are examined. Benefits like greater capacity and adherence to laws like GDPR are balanced against drawbacks like lock-in of vendors, latency, and threats to security. Practical implementations are demonstrated through actual-life instances from sectors such as healthcare and banking. The chapter ends with suggestions for practitioners and a summary of upcoming developments, such as AI-driven federated learning. This thorough review gives readers the skills they need to create and implement reliable multi-cloud federated platforms.