Transforming Legacy Data Systems to Modern Big Data Platforms Using Hadoop
Keywords:
Legacy data systems, big data platforms, Hadoop, Hadoop Distributed File System (HDFS), MapReduce, data migrationAbstract
The transition from traditional legacy data systems to contemporary big data platforms presents a significant opportunity for organizations seeking enhanced data management and analytical capabilities. This paper explores the migration process to modern big data frameworks, with a particular focus on utilizing Hadoop—a widely adopted open-source framework designed for scalable and fault-tolerant data processing. Legacy systems, often characterized by rigid architectures and limited scalability, struggle to accommodate the increasing volume, velocity, and variety of data generated in today’s digital landscape. Hadoop, with its distributed storage and processing capabilities, offers a robust solution to address these challenges.
This study investigates the core components of Hadoop, including Hadoop Distributed File System (HDFS) and MapReduce, and their roles in facilitating seamless data integration, storage, and processing. The paper highlights key strategies for successful migration, such as data assessment, system compatibility evaluation, and incremental implementation. Additionally, it examines case studies where organizations have leveraged Hadoop to modernize their data infrastructure, resulting in improved data accessibility, real-time analytics, and operational efficiency.
By delineating the benefits and addressing the complexities associated with transitioning to Hadoop, this paper aims to provide a comprehensive guide for organizations contemplating a shift from legacy systems to big data platforms. The insights presented are intended to assist stakeholders in making informed decisions and optimizing their data management strategies in the era of big data.
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