Comparative Analysis of Apache Sqoop and Apache Spark for Efficient Data Transfer Between Relational Databases and Hadoop Distributed File System (HDFS)

Author(s)	Sainath Muvva
Country	United States
Abstract	With the growing adoption of big data technologies like Hadoop, many companies are overhauling their data infrastructure. A crucial aspect of this transition is the ability to transfer both transactional and analytical data from traditional relational database management systems (RDBMS) into the new ecosystem. This migration enables advanced data processing and facilitates deeper analytical insights. This paper focuses on exploring the various tools available for importing data from relational databases into the Hadoop Distributed File System (HDFS). It delves into the underlying mechanisms of these tools and highlights the key distinctions between them.
Keywords	HDFS, Sqoop, Spark, SQL Loaders
Published In	Volume 11, Issue 3, July-September 2020
Published On	2020-08-05
Cite This	Comparative Analysis of Apache Sqoop and Apache Spark for Efficient Data Transfer Between Relational Databases and Hadoop Distributed File System (HDFS) - Sainath Muvva - IJSAT Volume 11, Issue 3, July-September 2020. DOI 10.5281/zenodo.14288579
DOI	https://doi.org/10.5281/zenodo.14288579
Short DOI	https://doi.org/g8tx76

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