Citation:

Sainath Muvva, 2021. "Cloud-Native Data Engineering: Leveraging Scalable, Resilient, and Efficient Pipelines for the Future of Data", ESP Journal of Engineering & Technology Advancements 1(2): 287-292.

Abstract:

This paper delves into the rapidly evolving domain of cloud-native data engineering, investigating the innovative ways organizations harness cloud-native paradigms and cutting-edge technologies to construct data ecosystems that are not only scalable and resilient but also highly efficient. By examining the intricate interplay between microservices-based pipelines, containerization strategies, and serverless computing models, we uncover the core elements driving this transformation. The study illuminates emerging practices such as DataOps integration, data mesh architectures, and AI-augmented data governance, while also addressing the formidable challenges of skills gaps, multi-cloud complexity, and ethical considerations in data handling. Through a series of in-depth case studies and performance analyses, we offer actionable insights for organizations navigating the shift towards cloud-native data engineering, ultimately charting a course for the future of data management in an increasingly distributed and dynamic digital landscape.

References:

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Keywords:

Cloud-Native, Data Engineering, Scalable Pipelines, Resilient Data Systems, Data Pipelines, Data Architecture, Big Data, Cloud Computing, Data Scalability, Data Resilience.