Published Paper Details:

Data pipeline engineering plays a crucial role in the insurance industry, enabling the efficient extraction, transformation, and loading (ETL) of data to support various business processes. This paper presents a comprehensive and critical analysis of ETL frameworks, integration strategies, and scalability considerations within the insurance domain. The main objectives of this study are to examine the existing ETL frameworks used in the insurance industry, evaluate different integration strategies employed for seamless data flow, and analyze scalability challenges and solutions in data pipeline engineering. To achieve these objectives, a systematic literature review was conducted, collecting relevant scholarly articles, industry reports, and case studies. The collected data was analyzed using qualitative and quantitative techniques, allowing for a comprehensive assessment of ETL frameworks, integration strategies, and scalability considerations. The key findings of this paper highlight the strengths and weaknesses of popular ETL frameworks utilized in the insurance domain, emphasizing their performance, flexibility, and scalability. Furthermore, the analysis identifies various integration patterns and best practices, showcasing successful strategies employed in the industry. Additionally, the study presents scalability challenges faced by insurance data pipelines and explores techniques to address them effectively. The contributions of this research lie in providing insurance professionals, data engineers, and researchers with valuable insights into the selection of appropriate ETL frameworks, integration strategies, and scalability considerations. The findings serve as a guide to optimize data pipeline engineering processes, enhance data integration efficiency, and ensure scalability in the dynamic insurance environment. In conclusion, this paper offers a critical analysis of ETL frameworks, integration strategies, and scalability in the insurance industry. The study's outcomes contribute to the body of knowledge in data engineering and provide practical recommendations for improving data pipeline efficiency and effectiveness in the insurance domain.