Key Challenges and Strategies in Managing Databases for Data Science and Machine Learning

Sethu Sesha Synam Neeli

doi:10.5281/zenodo.14672937

Key Challenges and Strategies in Managing Databases for Data Science and Machine Learning

Author(s)	Sethu Sesha Synam Neeli
Country	United States
Abstract	The convergence of data science and machine learning (ML) methodologies with enterprise-level data management systems necessitates a paradigm shift in database administration (DBA) practices. This integration presents significant hurdles, including the need for high-throughput data storage solutions (e.g., distributed NoSQL databases, columnar databases), real-time data streaming architectures (e.g., Apache Kafka, Apache Flink), robust data governance frameworks to ensure data quality and compliance (e.g., implementing data lineage tracking, metadata management), efficient management of heterogeneous data sources via ETL/ELT processes, and optimization strategies to mitigate the performance impact of ML model deployment and inference (e.g., model caching, query optimization techniques). Addressing these challenges requires a multi-faceted approach. This includes leveraging scalable database architectures (e.g., sharding, replication), implementing automated data manipulation and transformation processes (e.g., scripting with Python, leveraging cloud-based ETL services), and enforcing stringent security protocols using encryption, access control lists (ACLs), and intrusion detection systems. Furthermore, continuous professional development is crucial, encompassing expertise in areas such as AI-driven database auto-tuning, cloud-native database services (e.g., AWS RDS, Azure SQL Database, Google Cloud SQL), and containerization technologies (e.g., Docker, Kubernetes) for deploying and scaling ML workflows. By adopting these best practices, DBAs can ensure the efficiency, reliability, and scalability of data infrastructures essential for successful data science and ML initiatives
Keywords	DBMS, ML/AI, Scalability, Data Security & Compliance, Automation & Orchestration
Field	Engineering
Published In	Volume 2, Issue 3, March 2021
Published On	2021-03-10
Cite This	Key Challenges and Strategies in Managing Databases for Data Science and Machine Learning - Sethu Sesha Synam Neeli - IJLRP Volume 2, Issue 3, March 2021. DOI 10.5281/zenodo.14672937
DOI	https://doi.org/10.5281/zenodo.14672937
Short DOI	https://doi.org/g8z63s

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Key Challenges and Strategies in Managing Databases for Data Science and Machine Learning

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