Abstract

In contrast to these new-generation techniques, the evolution of data generation also introduced new issues in terms of data quality preservation, mainly on cloud-based Extract, Transform, and Load pipelines. Real-time stream anomaly detection has been pivotal to correcting the decision-making process, as inconsistent data could provide false positives of decision-making. This paper deals with the various machine learning-based techniques applied in anomaly detection concerning real-time stream data. The paper investigates supervised, unsupervised, and hybrid models for their accuracy, scalability, and computational efficiency. The paper's findings are benchmarked against synthetic and real-world datasets, providing actionable insights for practitioners and researchers. Anomaly detection mechanisms are integrated within cloud-native architectures, and some of the challenges include data latency, system scalability, and model interpretability. With vast experimentation and analysis, this research establishes the best practices to achieve data quality and integrity in various real-time environments, which further opened paths for improvements in automated ETL processes. It further explores the implications of anomaly detection on high-speed data streams. In doing so, it develops the trade-offs between model complexity and detection latency. This paper describes strategies to develop scalable, fault-tolerant cloud infrastructure supporting these methods, utilizing microservices and containerization technologies. The comparative framework not only evaluates classic techniques but also explores emerging techniques, which include graph-based and ensemble models, to overcome the shortcomings of existing approaches. The extensive information discussed is intended to assist practitioners in choosing the most suitable tools and configurations for anomaly detection, ultimately enhancing the robustness and reliability of cloud-based ETL pipelines.

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