Mastering Airflow: Building an ETL Pipeline

Data collection from multiple sources requires automation, scheduling, and reliability to avoid manual errors and ensure consistent updates. Apache Airflow is the industry-standard platform for orchestrating ETL pipelines (extract, transform, load), enabling data teams to schedule workflows, manage dependencies, and monitor execution across cloud environments. Mastering Airflow is essential for data engineers building scalable data pipelines that integrate with databases, APIs, and data lakes.

In this project, we'll build an automated ETL pipeline using Python, Pandas, and Apache Airflow that collects data from multiple sources, stores it in a data lake structure, and organizes it into scheduled Airflow DAGs (Directed Acyclic Graphs). We'll handle two data collection patterns: snapshot data captured at specific points in time, and time-based data collected continuously. For each pattern, we'll implement the complete ETL process: extracting raw data, saving it to a raw folder, transforming and cleaning it with Pandas, and transferring refined data to production storage.

We'll then automate these workflows by building Airflow DAGs with task dependencies, adding DAG parameters for flexibility, and implementing missing data detection and backfilling for stock data gaps. We'll optimize performance, configure Airflow variables for dynamic settings, and set up access control for DAG management. By the end, you'll have a production-ready data pipeline demonstrating Apache Airflow orchestration, ETL workflow automation, Pandas data transformation, DAG scheduling, and pipeline monitoring applicable to any data engineering or data warehousing project.