International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Scalable Kubernetes-Based Data Engineering Pipelines with Airflow and Kubeflow for Industrial IoT Analytics

Author(s) Urvangkumar Kothari
Country United States
Abstract Industrial Internet of Things (IIoT) is producing huge amounts of sensor and machine data, which needs robust scalable, and efficient data playbooks. After reading the present paper, the reader should have an understanding of how to build and implement a modular and cloud-native data engineering pipeline architecture, based on Kubernetes but which leverages Apache Airflow as a workflow orchestration tool, and Kubeflow as a machine learning lifecycle management tool. The given architecture responds to the actual requirements of IIoT settings: their velocity of data, heterogeneity of origins, and analysis in real-time. Through Kubernetes dynamic scaling and container orchestration features, fault tolerance and optimization of resources is maintained by the system. Ingestion and transformation of data is carried out using airflow and predictive models can be easily deployed and retrained using Kubeflow. We consider the reliability and performance of the system in a factory environment and suggest the principles of pipeline best practice design to benefit high-scale systems in terms of reliability, elasticity and maintainability. This strategy has shown great prospects of organizations that intend to implement analytics and machine learning in their industrial sectors to use open-source, large-scale infrastructure.
Keywords Industrial IoT (IIoT), Kubernetes, Apache Airflow, Kubeflow, Data Engineering Pipelines, Machine Learning, Workflow Orchestration, Real-Time Analytics, Scalability, Edge-to-Cloud Infrastructure.
Field Engineering
Published In Volume 3, Issue 6, November-December 2021
Published On 2021-12-08
DOI https://doi.org/10.36948/ijfmr.2021.v03i06.51992
Short DOI https://doi.org/g9tzrt
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