Modernizing Data Infrastructure: Migrating Hadoop Workloads to AWS for Scalability and Performance
Keywords:
Hadoop Migration, AWS Cloud, Big Data, EMRAbstract
As organizations gather and examine enormous amounts of data, the need for scalable, high-performance infrastructure has surged to formerly unheard-of heights. Though they are durable, conventional Hadoop-based systems sometimes run across operational complexity, scalability limitations, and high maintenance costs. Administering on-site Hadoop clusters requires significant resources, including hardware procurement and constant management, therefore making it an expensive and cumbersome solution in the present fast digital scene. Moving Hadoop chores to the cloud makes a good case especially in AWS. AWS offers a range of cloud-native products with a view toward cost reducing, scalability, and performance enhancement. Elastic MapReduce and Amazon E MR help to efficiently control huge data loads free from the operational weight of manual cluster deployment. Services such Amazon S3, AWS Glue, and AWS Lambda further help to maximize data storage, transformation, and analytics. This article analyzes important migration decisions for transferring Hadoop workloads to AWS using managed services, re-architecting for cloud-native solutions, and lift-and-shift approaches. We will look at best ways to enable a smooth transition including security considerations, performance improvement, and cost-effective approaches. Changing their data architecture with AWS can help businesses to quickly find new insights, reduce operational loads, and improve flexibility. Whether you are building a cloud-first data strategy or managing legacy Hadoop clusters, this paper provides practical advice for effectively negotiating the conversion process.
Downloads
References
Divate, Rishi, Sankalp Sah, and Manish Singh. "High performance computing and big data." Guide to big data applications (2018): 125-147.
Shekhar, Suman. "An in-depth analysis of intelligent data migration strategies from oracle relational databases to hadoop ecosystems: Opportunities and challenges." Internafional Journal of Applied Machine Learning and Computafional Intelligence 10.2 (2020): 1-24.
Genovese, Simona. Data Mesh: the newest paradigm shift for a distributed architecture in the data world and its application. Diss. Politecnico di Torino, 2021.
Russom, Philip. "Data warehouse modernization." TDWI Best Pract Rep (2016).
Orozco-GómezSerrano, Aldo. "Adaptive Big Data Pipeline." (2020).
Manchana, Ramakrishna. "Operationalizing Batch Workloads in the Cloud with Case Studies." International Journal of Science and Research (IJSR) 9.7 (2020): 2031-2041.
Kandalam, Phani Vivekanand. "Data Warehousing Modernization: Big Data Technology Implementation." (2016).
Raza, Ali, and Waseem Ahmed Khattak. "Developing Scalable Data Infrastructure for Retail E-Commerce Growth in Emerging East Asian Markets." Journal of Human Behavior and Social Science 6.7 (2022): 32-41.
Pathak, Ajeet Ram, Manjusha Pandey, and Siddharth S. Rautaray. "Approaches of enhancing interoperations among high performance computing and big data analytics via augmentation." Cluster Computing 23.2 (2020): 953-988.
Seenivasan, Dhamotharan. "Transforming Data Warehousing: Strategic Approaches and Challenges in Migrating from On-Premises to Cloud Environments." (2021).
Laszewski, Tom, et al. Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packt Publishing Ltd, 2018.
Settu, Rajaraajeswari, and Pethuru Raj. "Cloud application modernization and migration methodology." Cloud Computing: Methods and Practical Approaches (2013): 243-271.
Eagar, Gareth. Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS. Packt Publishing Ltd, 2021.
Seenivasan, Dhamotharan. "Distributed ETL Architecture for Processing and Storing Big Data." (2022).
Raj, Pethuru, et al. "High-performance big-data analytics." Computing Systems and Approaches (Springer, 2015) 1 (2015).
