When organizing a conference, we often encounter questions such as how to set an appropriate ticket price, how many t-shirts to produce, and how many lunch boxes to estimate. Frequently, our estimates rely on wild guesses, which can lead to inaccuracies. To tackle this issue, we aimed to establish PyCon Taiwan as a data-driven community and formed a data team back in 2021. As the Python community continues to grow with an increasing number of data professionals, this presents a great opportunity for us to attract more volunteers to join PyCon Taiwan.

In our community, we offer an excellent experimental playground for data enthusiasts to test their ideas and explore new technologies. In this talk, I will share how PyCon Taiwan built our internal ETL system using Apache Airflow and discuss our plans to migrate to the soon-to-be-released Airflow 3.0. Additionally, I will explain how we collect data and how it helps us make decisions based on evidence rather than gut feelings.