COMPARATIVE STUDY OF MULTI-CLOUD DATA ENGINEERING STRATEGIES

Abstract

Best practices from data engineering processes have been significantly modified in disparate sectors due to multi-cloud solutions only, involving multiple cloud service providers (CSP). Multi-cloud data engineering enables businesses to distribute workloads across different cloud platforms, increasing performance, reducing the potential for vendor lock-in, and supporting scalability. Notably will be covered and assessed in this study that pertains to multi-cloud data engineering are: Data integration Security Cost optimisation and System interoperability. The report begins with a historical overview of cloud computing and multi-cloud approaches that highlight why organizations have adopted these setups. Part of the reason for this is the need to comply with data rules throughout many countries, along with requirements for more resilience and flexibility. The study covers fundamentals of multi-cloud architecture such as data replication, workload allocation, and resource management. It explores data engineering practices of various companies operating with focus scheduled optimization resources, merging multiple data sources, and defining metrics for cloud service selection. The research also compares managing data pipelines for ingestion to transformation, storage, and processing across clouds with cloud-native versus third-party tools. When handling information across several clouds, protecting sensitive material and ensuring compliance rules at every data engineering level is of the biggest significance. This paper is a twofold concern; one is looking on security, and another on compliance.