Open Access

ARTICLE

An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

by Norwini Zaidi¹, Iskandar Ishak^2,*, Fatimah Sidi², Lilly Suriani Affendey²

1 System Development and Engineering Center, Universiti Sains Islam Malaysia, 71800, Negeri Sembilan, Malaysia
2 Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, UPM Serdang, 43400, Selangor, Malaysia

* Corresponding Author: Iskandar Ishak. Email:

Computer Systems Science and Engineering 2022, 43(3), 1175-1188. https://doi.org/10.32604/csse.2022.021969

Received 22 July 2021; Accepted 23 September 2021; Issue published 09 May 2022

Abstract

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However, there is no standard guideline for data transformation from relational database to NoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processing time when compared to the relational database query processing time. However, these approaches produced redundant tables in the resulted schema that in turn consume large unnecessary storage size and produce high query processing time due to the generated schema with redundant column families in the transformed column-oriented database. In this paper, an efficient data transformation technique from relational database to column-oriented database is proposed. The proposed schema transformation technique is based on the combination of denormalization approach, data access pattern and multiple-nested schema. In order to validate the proposed work, the proposed technique is implemented by transforming data from MySQL database to HBase database. A benchmark transformation technique is also performed in which the query processing time and the storage size are compared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storage space usage due to the reduced number of column families in the column-oriented database.

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Keywords

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