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Advances in Data Migration Tools for Cloud Billing Systems

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  • Ravi Mani

Abstract

This article examines advancements in data migration tools for cloud billing systems. It highlights their impact on industries like healthcare, insurance, and telecommunications. It examines the key challenges faced during the migration of legacy billing systems to cloud platforms, including ensuring data accuracy, maintaining regulatory compliance, and minimizing operational downtime. The article delves into innovative solutions such as AI-powered ETL pipelines, automated data validation techniques, and metadata-driven frameworks, highlighting their features and benefits. Through case studies in healthcare and telecommunications, the article demonstrates the practical applications and outcomes of these advanced tools. Emerging trends like hybrid cloud migration, real-time data synchronization, blockchain for data integrity, and no-code/low-code platforms are discussed, offering insights into the future direction of billing system migrations. The article also addresses persistent challenges in data complexity, system downtime, and regulatory compliance, proposing strategic solutions. Finally, it explores future directions, including predictive analytics for risk identification, deeper integration of blockchain technology, and the potential of serverless architectures in data migration. This comprehensive article overview provides valuable insights for organizations planning or undertaking cloud billing system migrations, emphasizing the importance of adopting cutting-edge tools and strategies to ensure successful transitions in an increasingly digital landscape.

Suggested Citation

  • Ravi Mani, 2025. "Advances in Data Migration Tools for Cloud Billing Systems," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 1130-1139, February.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:773
    DOI: 10.32628/CSEIT251112117
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112117
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