Designing Robust ETL Pipelines with PySpark and AWS Glue for Scalable Deal Intelligence

Large-scale and stable ETL (Extract, Transform, Load) pipelines have become necessary in the age of data-driven decision-making when organizations are seeking to elicit actionable understanding from the huge and varied source of data. This review paper will look at the current architectural and technological advancement of ETL pipelines, especially the application of PySpark and AWS Glue as part of the cloud platform as a mechanism of supporting a scalable deal intelligence platform. It addresses the design of old-style ETL to novel AI-centric and declarative approaches, the significance of automation techniques founded with foundation models, and the performance optimization techniques of high-scale data processing and addresses the particular problems in deal intelligence, including schema drift, adherence, and non-homogenous data. In comparison to the current literature, the framework injects ML-based schema inference and rule-based coordination, redesigns the pipeline structure to propel financial analytics, and considers the role of security and governance and serverless orchestration in resiliency when pipelines have to transform to dynamic financial data conditions. The paper offers a detailed understanding of how PySpark and AWS Glue can be implemented to develop effective data engineering processes to address high-value deal apprehensions by an analytical analysis of the current trends and functionalities.