Stage 1 · Lesson 6 of 8
Python for pipelines
Python is the glue language of data engineering. You use it to call APIs, read and write files, and talk to databases and cloud services. You also use it to write Airflow DAGs (pipeline definitions) and Spark jobs.
Focus on the parts pipelines actually need. That means functions and modules, (a separate set of libraries per project), reading CSV and JSON, and dates and time zones. It also means logging instead of print, handling errors and retrying, and .
Learn for small data. Know that DuckDB or Polars handle larger files on your own machine far faster. Write your code as small functions you can test, not one long .
Common trapstaying in notebooks: a pipeline has to run alone at 3 a.m., so it needs to be a script with logging, configuration and a clear .
A question to check yourself, an AI practice session and 2 cheat sheet entries come with the full roadmap. Unlock
Resources for this stage
Start with· follow one of these
- CourseFreeData Engineering Zoomcamp
DataTalks.Club · Beginner
Free, cohort-based or self-paced course: Module 1 covers Docker, PostgreSQL and loading real taxi data
- CourseFreeCS50's Introduction to Programming with Python
Harvard University · Beginner
If your Python is shaky, work through the lectures on functions, exceptions, file I/O and libraries first