In the world of data science, raw data serves as the foundation for generating actionable insights. However, managing, processing, and transforming this data into a usable format requires specialized tools. Data engineering plays a pivotal role in the data science workflow by ensuring data availability, quality, and reliability. To achieve these goals, professionals rely on advanced tools tailored to meet the diverse challenges of handling large and complex datasets.