ETL Architecture Patterns: Hub-and-Spoke, Lambda, and More
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P107Keywords:
ETL, Data Engineering, Hub-and-Spoke, Lambda Architecture, Kappa Architecture, Data Pipelines, Data Integration, Real-time Analytics, Data WarehousingAbstract
For companies depending on fast insights in the present data-centric world, the building of scalable and effective data pipelines is very crucial. This article looks at many ETL (Extract, Transform, Load) architecture solutions that meet different operational and analytical needs and help these pipelines. We begin by examining the classic Hub-and--Spoke model, which is suitable for companies that stress control and sustainability as it is known for centralized governance and modular construction. We explore contemporary paradigms like the Lambda and Kappa architectures, which enable batch and actual time processing, in view of the increasing data velocity and complexity. While Kappa architecture simplifies design by relying only on streaming, Lambda architecture offers robustness by means of the combination of batch and streaming layers, at the expense of code duplication. We also go over creating microservices-based ETL solutions fit for cloud-native, containerized systems and hybrid models. By analyzing these trends in terms of scalability, latency, fault tolerance, and the actual world adaptation, this article offers pragmatic understanding of the ideal conditions for every approach. Emphasizing the trade-ins in performance, maintainability, and price, a case study from a data-driven company shows the pragmatic results of choosing one design over one another. Whether they are creating the latest data solutions or upgrading these current systems, this extensive overview seeks to assist data architects, engineers, and decision-makers in making informed, strategic choices on data integration
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