In today's data-driven world, organizations face the challenge of efficiently processing, analyzing, and deriving insights from large volumes of diverse data sources. Data ingestion pipelines play a crucial role in the overall data processing flow by facilitating the collection of data from various sources and making it ready for further analysis. As part of this pipeline, data transformation and enrichment processes significantly enhance data quality, standardization, and enrichment, ultimately leading to better insights. This academic journal aims to explore the importance of data transformation and enrichment processes as part of the ingestion pipeline, discuss various strategies and techniques, highlight real-world implementations and their impact, and delve into challenges and best practices for effective implementation.