Automating ETL Processes in Modern Cloud Data Warehouses Using AI

Authors

  • Guruprasad Nookala JP Morgan Chase, USA
  • Kishore Reddy Gade JP Morgan Chase, USA
  • Naresh Dulam JP Morgan Chase, USA
  • Sai Kumar Reddy Thumburu Asea Brown Boveri, Sweden

Abstract

The evolution of cloud data warehouses has drastically transformed data management, making ETL (Extract, Transform, Load) processes more efficient, scalable, and adaptable. However, as the volume and complexity of data grow, traditional ETL workflows can struggle to keep up, often leading to inefficiencies and errors. This article explores the automation of ETL processes in modern cloud data warehouses using artificial intelligence (AI). By leveraging AI technologies, organizations can streamline their ETL pipelines, reducing manual intervention, improving data accuracy, and accelerating data integration workflows. AI-driven ETL tools enable predictive data transformation, dynamic schema mapping, and real-time data integration, which can adapt to changes in data structures or business requirements. These tools not only boost productivity but also ensure data quality by automating error detection and anomaly handling. The integration of machine learning algorithms further enhances these processes by learning from historical data patterns and optimizing the ETL logic over time. This shift towards intelligent automation in ETL also addresses challenges in handling unstructured or semi-structured data, making it easier for organizations to manage diverse data types within their cloud environments. The article also discusses the role of AI in scaling ETL processes to support big data analytics, allowing organizations to tap into real-time insights and make data-driven decisions faster. Finally, the article covers case studies of companies that have successfully implemented AI-automated ETL in their cloud data warehouses, demonstrating improved efficiency, lower operational costs, and enhanced data governance.

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Published

2020-11-17

How to Cite

Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2). Retrieved from http://mzresearch.com/index.php/MZCJ/article/view/431

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