Big Data and the Data Engineering Challenges in Multi-Cloud Environments: Addressing Data Integration and Compliance

Approved

Relevance and Originality:

This research article is highly relevant in the context of the growing adoption of multi-cloud environments by enterprises, which are increasingly seeking to optimize big data storage, processing, and analytics. The focus on the challenges faced by data engineers when managing data in multi-cloud environments is both timely and crucial, given the rapid development of cloud technologies and their integration into business operations. The paper's exploration of issues such as data integration, security, compliance, and governance in multi-cloud settings presents original insights, especially as these challenges are increasingly complex in today's fast-evolving technological landscape. The inclusion of emerging trends like hybrid and edge computing further enhances the novelty of the research, making it a valuable contribution to the field.

Methodology:

The methodology used in the paper is centered around a comprehensive review of tools, technologies, and best practices, supplemented by case studies and research. While the theoretical exploration of these issues is extensive, the paper could benefit from a more systematic approach to data collection and analysis, particularly in terms of providing quantitative data or in-depth empirical evidence on how the proposed solutions are applied in real-world scenarios. Case studies are helpful, but a more detailed exploration of each organization's approach, challenges, and outcomes would strengthen the paper's practical applicability. Furthermore, clearer details on how the research was conducted—whether through surveys, interviews, or other empirical methods—would enhance the credibility of the findings.

Validity & Reliability:

The research offers a solid theoretical foundation for addressing the challenges of managing big data in multi-cloud environments, but its reliance on case studies and general research limits its empirical rigor. The findings are logically supported, but the article would be more robust if the case studies were backed by more detailed data and analysis. The paper provides useful insights, but the lack of empirical validation, such as statistical analysis or broader industry surveys, affects the generalizability and robustness of the conclusions. While the insights are valuable, they could be strengthened by more concrete evidence supporting the effectiveness of the proposed tools and practices in diverse organizational contexts.

Clarity and Structure:

The article is well-organized, with a clear structure that leads the reader from an overview of the challenges of multi-cloud data management to proposed solutions and emerging trends. The writing is clear and accessible, with technical concepts explained in a straightforward manner, making the paper suitable for both technical and non-technical readers. However, certain sections could be expanded to provide further context or examples, particularly regarding complex topics such as data consistency and latency in multi-cloud environments. A more detailed explanation of specific tools and technologies used by data engineers would also improve the clarity of the recommendations. Overall, the structure and flow of the paper make it easy to follow and understand.

Result Analysis:

The analysis of multi-cloud data management challenges is comprehensive, covering critical issues such as integration, security, governance, and compliance. The solutions proposed are practical and well-aligned with current industry needs, offering useful recommendations for overcoming these obstacles. However, the analysis could be more detailed, especially in evaluating the effectiveness of different tools and techniques in various multi-cloud scenarios. While the case studies provide some insights into real-world applications, a deeper dive into the outcomes and lessons learned from these examples would provide more valuable insights into how organizations have successfully addressed these challenges. Additionally, a more critical evaluation of the limitations of current solutions and potential gaps in existing research would strengthen the overall analysis.