This repository contains my implementation of the End-to-End Data Engineering Project from LinkedIn Learning, instructed by Thalia Barrera.

This project demonstrates how to implement an end-to-end data engineering pipeline using tools from the modern data stack, including AirByte, DBT, and Dagster.

• Data Modeling: Best practices for organizing data into logical structures.
• Testing & Documentation: Techniques for ensuring data quality and maintaining proper documentation.
• Version Control: Managing changes to data pipelines with proper version control methods.
• Extract, Load, Transform (ELT): Efficient methods to extract, load, and transform data into a unified, analytics-ready format.

• AirByte: For extracting and loading data.
• DBT: For transforming data into a structured and analytics-ready state.
• Dagster: For orchestrating and managing the end-to-end pipeline.

You can view my certificate of completion here.