I Thought Data Engineering Was Just Writing Scripts. I Was Wrong.

A data analyst transitioning to data engineering shares lessons learned from making a simple ETL pipeline more robust. The initial Python script, which pulled data from the GitHub API, broke when confronted with real-world challenges like data duplication and persistence. The author solved these issues by implementing idempotency, switching from a CSV file to a SQLite database, and storing the data on Google Drive to ensure it survives sessions. This experience revealed that data engineering is fundamentally about building reliable, automated systems, not just writing scripts, highlighting the need for tools like schedulers.