In the era of data-driven decision-making, the performance and scalability of ETL (Extract, Transform, Load) data pipelines are crucial for managing large and complex datasets efficiently. This study explores the design and testing of an ETL data pipeline built with Apache Airflow, Python Pandas, and Pytest. Airflow orchestrates pipeline workflows, ensuring transformation dependencies and scheduling are managed correctly. Pandas handles data manipulation, offering robust tools for efficient transformations. Pytest enables a structured framework for unit testing, ensuring reliability, performance and scalability under varying data loads.
The study provides technique for data performance and scalability testing by simulating diverse workloads to identify bottlenecks and optimize execution time. Metrics such as records per second or data points per second (optionally resource utilization) are evaluated via not always provided system performance and scalability expectations.
Key Takeaways:- Gain knowledge of creating ETL Airflow data pipeline with Python Pandas library
- Know how to test performance and scalability of ETL data pipeline by designing/executing/reporting tests via Python Pytest library
- Getting familiar with tips and tricks in Python IDE (Pycharm)
