Automating Data Engineering Workflows with AI and Machine Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P102Keywords:
AI in Data Engineering, Machine Learning, Data Quality, Data Pipelines, Automation, Big Data, Schema Inference, Pipeline Optimization, Ethical AI, Real-Time ProcessingAbstract
Data engineering is a critical component of modern data-driven organizations, encompassing the extraction, transformation, and loading (ETL) of data, as well as the management and optimization of data pipelines. The increasing volume, velocity, and variety of data pose significant challenges for data engineers, who must ensure that data is accurate, timely, and available for various downstream applications. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) techniques to automate and optimize data engineering workflows. We discuss the current state of data engineering, the challenges faced by data engineers, and the potential benefits of AI and ML in addressing these challenges. We present several case studies and algorithms that demonstrate the effectiveness of AI and ML in automating data engineering tasks, including data quality assessment, schema inference, and pipeline optimization. Finally, we discuss the ethical and practical considerations of deploying AI in data engineering and provide recommendations for future research and development
References
[1] https://www.researchgate.net/figure/The-machine-learning-engineering-workflow_fig3_365808973
[2] https://atlan.com/automation-for-data-engineering-teams/
[3] https://dataengineeracademy.com/blog/automating-etl-with-ai/
[4] https://nexla.com/data-engineering-best-practices/data-engineering-automation/
[5] https://interviewkickstart.com/blogs/learn/automating-data-workflows-ai-prompt-engineering
[6] https://www.tredence.com/blog/data-engineering-automation
[7] https://www.linkedin.com/pulse/ai-driven-data-engineering-automating-pipelines-machine-kdrcc
[8] https://www.acceldata.io/blog/automation-in-data-engineering-essential-components-and-benefits
[10] Chen, J., & Zhang, Y. (2020). Automated Data Quality Assessment Using Machine Learning. Journal of Data Engineering, 12(3), 45-58.
