Journal of Advances in Developmental Research

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Optimizing Large-Scale Data Transfers with Hadoop’s distcp: Use Cases and Best Practices

Author(s) Pavan Kumar Mantha
Country United States
Abstract In the era of big data, efficient and reliable large-scale data transfers are crucial for maintaining seamless operation in distributed systems. Hadoop's Distributed Copy (distcp) tool has emerged as the cornerstone for transferring massive datasets across clusters, cloud storage platforms, and hybrid environments [1]. This study provides an in-depth exploration of distcp, focusing on its general functionality, key options, and configurations to optimize performance. We delve into various optimization techniques, including incremental updates, bandwidth control, and parallelism tuning, which are essential for achieving scalable fault-tolerant data migration [1][2]. Practical use cases and best practices are discussed to illustrate how distcp supports diverse workflows from disaster recovery to hybrid cloud synchronization. By leveraging these strategies, data engineers can enhance the efficiency and reliability of large-scale data transfers, thereby paving the way for robust and scalable data pipelines.
Keywords big data, distributed systems, Hadoop, distributed copy, data migration, bandwidth optimization, parallelism tuning, fault tolerance
Field Engineering
Published In Volume 9, Issue 1, January-June 2018
Published On 2018-05-04
Cite This Optimizing Large-Scale Data Transfers with Hadoop’s distcp: Use Cases and Best Practices - Pavan Kumar Mantha - IJAIDR Volume 9, Issue 1, January-June 2018. DOI 10.71097/IJAIDR.v9.i1.1452
DOI https://doi.org/10.71097/IJAIDR.v9.i1.1452
Short DOI https://doi.org/g9q34h
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