Modern Data Engineering Using Uber’s Data Set

In the age of extensive data, deriving useful insights from large datasets has become a critical part of decision-making across various fields, notably in sectors such as transportation, where platforms like Uber generate vast quantities of data. This research aims to create a comprehensive data engineering process for analysing Uber’s dataset using services provided by the Google Cloud Platform (GCP). The proposed system comprises a data warehouse, Python for data manipulation, Mage for coordination, Google BigQuery for data storage, and Looker for visualization. By utilizing cloud-based infrastructure and open- source tools, this system offers adaptability, scalability, and dependability, effectively addressing the changing requirements of data analysis in today’s digital realm. This endeavor exemplifies how contemporary data engineering methods can be employed to extract valuable insights from intricate datasets, ultimately empowering organizations to excel in the data- centric era.