The Evolution of Big Data Workflows: From On-Premise Hadoop to Cloud-Based Architectures

Author(s)	Naga Surya Teja Thallam
Country	United States
Abstract	Due to the rapid expansion of digital data, scalable and efficient big data processing architectures are gained since demand. Hadoop is, initially, used on premise, but as the cloud development grows, organizations start using cloud infrastructures, which allow them to use scalability, lower cost, and real time analytics. The focus of this paper is on this shift in solving movement from traditional on premise Hadoop ecosystem to cloud native alternatives and exploring the challenges and opportunities of such transition. This includes limitations of Hadoop based workflows, advancement in cloud computing which has helped in this migration and a comparative assessment of various cloud based big data solutions. The migration strategies, challenges and best practices are also discussed in the study to assist enterprises in modernizing their data infrastructure. This research provides insights into cloud adoption frameworks to help organizations go well beyond their big data processing capabilities, but in a cost effective and performance improving manner.
Keywords	Big Data, Hadoop, Cloud Computing, Distributed Computing, Data Processing, Cloud Migration, Apache Spark, Serverless Computing, Data Analytics, Cloud-Native Architectures.
Field	Computer > Design
Published In	Volume 2, Issue 5, September-October 2020
Published On	2020-10-08

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International Journal For Multidisciplinary Research