Paper Key : IRJ************715
Author: Anandaganesh Balakrishnan
Date Published: 09 Mar 2024
Abstract
In the evolving AI landscape, Data Engineering is a key enabler of organizational value, crucial for creating and maintaining datasets and data products vital for advanced analytics. Data engineers are central to fostering data-driven decision-making by building and managing data collection, storage, processing, and analysis infrastructure. The advent of AI technologies such as Retrieval Augmented Generation (RAG), Reinforcement Learning from Human Feedback (RLHF), and Fine-tuning opens new paths to speed up the development of data engineering pipelines, enhancing efficiency across organizational operations. This paper explores how RAG, RLHF, and fine-tuning can synergistically optimize and streamline data engineering processes, resulting in quicker and more efficient data pipeline generation. It examines methodologies across the data engineering lifecycle, including data collection, processing, storage, quality, analytics, and security, and demonstrates how these AI techniques can automate and improve data engineering tasks. By detailing practical applications and the transformative potential of these technologies, the paper aims to offer insights into creating efficient data engineering pipelines that align with the demands of modern data infrastructure, empowering organizations to leverage their data fully in an AI-driven era.
DOI LINK : 10.56726/IRJMETS50070 https://www.doi.org/10.56726/IRJMETS50070