Optimizing Data Engineering for AI Applications: A Case Study in Predictive Analytics

Abstract

In the era of big data and artificial intelligence (AI), optimizing data engineering practices is crucial 
for enhancing the efficiency and effectiveness of predictive analytics applications. This paper 
presents a case study focused on optimizing data engineering pipelines to support AI-driven 
predictive analytics. We explore strategies for improving data quality, processing speed, and 
scalability to ensure robust and accurate predictive models. The case study involves a 
comprehensive analysis of data ingestion, transformation, and storage techniques tailored to AI 
requirements. By implementing advanced data engineering practices, including automated ETL 
processes, data lake architectures, and real-time data streaming, we demonstrate significant 
improvements in model performance and operational efficiency. Our findings highlight the 
importance of aligning data engineering workflows with AI objectives and provide actionable 
insights for organizations seeking to leverage predictive analytics for strategic decision-making.