Challenges and Best Practices for Database Administration in Data Science and Machine Learning

Authors: Balakrishna Boddu

DOI: https://doi.org/10.5281/zenodo.14059355

Short DOI: https://doi.org/g8qjpf

Country: USA

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Abstract: The integration of data science and machine learning (ML) into various industries has significantly transformed data management and analysis. However, this integration presents new challenges for database administrators (DBAs). This paper explores the critical obstacles faced by DBAs, including the need for efficient data storage, real-time data processing, ensuring data quality and security, managing large-scale, heterogeneous datasets, and addressing the performance overhead associated with ML model integration.
To overcome these challenges, the paper outlines best practices such as adopting scalable database management systems, utilizing automated tools for data cleaning and integration, and implementing robust security protocols. Additionally, it emphasizes the importance of continuous learning and adaptation to new technologies, such as AI-driven database optimization and cloud-based solutions. By following these best practices, DBAs can enhance the efficiency and reliability of database systems, ultimately supporting the successful deployment of data science and ML applications.

Keywords: DBMS, ML,AL,Scalability,Data Security,Compliance, Automation and Orchestration

Paper Id: 231461

Published On: 2021-03-03

Published In: Volume 9, Issue 2, March-April 2021

Cite This: Challenges and Best Practices for Database Administration in Data Science and Machine Learning - Balakrishna Boddu - IJIRMPS Volume 9, Issue 2, March-April 2021. DOI 10.5281/zenodo.14059355

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