Abstract

In today’s evolving data landscape, organizations face challenges in integrating on-premises ETL (Extract, Transform, Load) systems with cloud-based ETL solutions. While on-premises ETL offers greater control and security, it lacks the scalability and flexibility of cloud ETL. However, a complete shift to the cloud is not always feasible due to data governance, compliance, and performance concerns. This article explores a hybrid ETL approach that bridges the gap between on-premises and cloud infrastructure, ensuring optimized data processing, cost efficiency, and seamless integration. Key challenges such as latency, security, and interoperability are discussed, along with best practices for achieving an effective hybrid ETL strategy. Additionally, real-world case studies highlight successful implementations. As enterprises continue their digital transformation journey, adopting a well-structured hybrid ETL framework can enable scalability, performance optimization, and long-term sustainability. This paper provides insights into future trends, helping organizations build resilient and adaptable ETL architectures.

Keywords

  • artifficial intelligence
  • machine learning
  • Personalised treatment plan

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