Cloud-Native ETL Automation: Leveraging AI/ML to Build Resilient, Self-Healing Data Pipelines
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V2I4P112Keywords:
Cloud-Native, ETL, Data Pipelines, AI/ML Automation, Self-Healing Systems, Data Engineering, Resiliency, Data Quality, Fault-Tolerant PipelinesAbstract
In the present day's fast-changing, data-driven world, traditional ETL (Extract, Transform, Load) techniques typically don't work well with the size, speed & the complexity of modern data ecosystems. This paper looks at how these cloud-native architectures, together with AI and ML, are changing ETL automation to create data pipelines that are more resilient, scalable, and able to fix themselves. Moving ETL processes to the cloud lets businesses shift away from rigid, monolithic infrastructures & adopt a modular, event-driven approach that changes with the information. The main idea behind this improvement is self-healing pipelines, which are systems that can find more issues, predict them, and fix them without any help from these people. The research looks at how AI and ML models may be used for things like finding more anomalies, adapting transformation logic, optimizing workloads, and fixing bugs intelligently. We focus on important patterns and technologies in these cloud-native ecosystems, including container orchestration, serverless computing, and the event streaming platforms like Apache Kafka. Together, they provide for a very responsive and cost-effective pipeline design. We show how engineers may increase their operational resilience by using cloud-native components and AI-driven observability using actual world design ideas and the architectural patterns. We look at how these technologies greatly reduce the amount of human monitoring and maintenance work that has to be done, freeing up engineering resources and speeding up the time it takes for business users to get insights. This study argues for updating ETL processes using a self-healing, AI/ML-based method that finds more problems before they happen, makes changes in the actual time, and makes sure that data is too reliable even while things are changing. This shift in the way things work will not only lead to little improvements, but also a big step forward in how these organizations manage, control, and trust their data pipelines.
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