Here is my latest data engineering project leveraging the power of Google Cloud and Mage! I recently completed an end-to-end data engineering project using the Uber dataset, and I'm thrilled with the outcomes. 📊💡
Here's a glimpse into the architecture I worked for this project:
📌 Data Ingestion: I started by uploading the Uber dataset to Google Cloud Storage, ensuring secure and reliable storage of the raw data.
📌 Data Transformation with Mage and Google Compute Engine: To perform the data transformation, I utilized Mage,Open-source data pipeline tool for transforming and integrating data. Mage's distributed processing capabilities allowed me to efficiently clean, transform, and enrich the dataset, ensuring its quality and integrity.
📌 Data Warehousing: The cleaned and transformed data was then loaded into Google BigQuery, a scalable and high-performance data warehousing solution, enabling fast and interactive analysis.
📌 Analytics and Insights: Leveraging the querying power of BigQuery and SQL, I conducted extensive analysis on the Uber dataset. I uncovered valuable insights related to user behavior, demand patterns, and performance metrics, facilitating data-driven decision-making.
📌 Visualization with Looker: To make the insights easily accessible and visually appealing, I used Looker, a robust data visualization and business intelligence platform. Looker allowed me to create interactive dashboards and visualizations, enabling stakeholders to gain intuitive and actionable insights.