ETL Testing in Cloud Environments: A Methodology for Agile Data Validation in Multitenant Architectures
Author(s): Santosh Kumar Vududala
Publication #: 2502061
Date of Publication: 05.08.2024
Country: USA
Pages: 1-16
Published In: Volume 10 Issue 4 August-2024
DOI: https://doi.org/10.5281/zenodo.14883099
Abstract
Today’s data driven business relies heavily on ETL (Extract, Transform, Load) processes, especially in the cloud where scalability, agility and multi tenancy are critical. A critical challenge in ensuring data accuracy, integrity, and reliability during ETL operations in cloud infrastructures is their dynamic nature and by extension complexities. A detailed methodology for ETL testing within agile development frameworks and multitenant cloud architecture is proposed in this paper. The methodology focuses on intelligent ways of defining and validating data automatically in real time, robust anomaly detection and test driven development to guarantee continuous data quality on the fly in moving environments. The proposed approach also supports agile data engineering principles, by integrating the use of advanced testing strategies such as data virtualization, synthetic data generation, and real-time monitoring, in order to perform rapid iterations and deployments. In addition, it also considers the particular difficulties associated with multitenant architectures (i.e., tenant data segmentation, security, and performance optimization to name a few). This approach is case studied and experimentally evaluated, demonstrating streamlined ETL testing process, reduced deployment risks, and increased data reliability. At its heart, this paper is about delivering to practitioners and researchers actionable insights on how to optimize ETL testing in cloud native environments in order to create robust data pipelines that dive the quality levels demanded by an enterprise when it comes to speed and precision.
Keywords: ETL testing, Cloud environments, agile data validation, Multitenant architecture, Data quality, Synthetic data
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