AUTOMATE DATA SCIENCE WORKFLOWS USING DATA ENGINEERING TECHNIQUES
The paper explores using WMS to incorporate ADP and AFT when implementing the entire data science pipeline, from data acquisition to deployment of the final model, to suggest that using data engineering approaches saves time and resources while performing data pre-processing and analysis, improves the quality and reliability of analytics findings and outputs, and is an essential component of contemporary analytical pipelines.
Abstract
This assignment focuses on applying data engineering practices in data science, aiming to improve the speed, size, and reproducibility of data-driven tasks. The paper explores using WMS to incorporate ADP and AFT when implementing the entire data science pipeline, from data acquisition to deployment of the final model. By analyzing simulation reports and real-life cases, this work showcases the effectiveness of automation in addressing issues including integration, time of processing, and reliance on manual efforts for enhancing decision-making and organizational processes. The main points suggest that using data engineering approaches saves time and resources while performing data pre-processing and analysis, improves the quality and reliability of analytics findings and outputs, and is an essential component of contemporary analytical pipelines. Ā