Python for Data Engineering
Learn to write Python pipeline scripts that run unattended: pandas, files, APIs, logging, tests, and idempotent writes, the glue of every data stack.
What You'll Learn
Why Python Runs Every Data Pipeline You Will Ever Build
You have just joined acme-shop's data team.
Setting Up a Python Data Pipeline Project
Before you write a transformation, you need a project that runs the same way on your laptop, your teammate's laptop, and the server that will...
Core Python Building Blocks for Pipeline Code
Every pipeline, however complex, is built from the same small set of Python pieces used carefully.
Working with Data Files - CSV, JSON, and Paths
Reading and writing CSV and JSON Keeping secrets in environment variables A secret is any value that must not be visible to anyone reading your code...
pandas - The Data Engineering Workhorse
pandas is a Python library built around the DataFrame, a table of rows and columns held in memory, like a spreadsheet or a SQL table.
Testing Pipeline Code with pytest
A function that looks right and a function that is verified right are different things.
Skills You'll Master
Curriculum Index11 topics
Why Python Runs Every Data Pipeline You Will Ever Build
You have just joined acme-shop's data team.
Setting Up a Python Data Pipeline Project
Before you write a transformation, you need a project that runs the same way on your laptop, your teammate's laptop...
Core Python Building Blocks for Pipeline Code
Every pipeline, however complex, is built from the same small set of Python pieces used carefully.
Working with Data Files - CSV, JSON, and Paths
Reading and writing CSV and JSON Keeping secrets in environment variables A secret is any value that must not be...
pandas - The Data Engineering Workhorse
pandas is a Python library built around the DataFrame, a table of rows and columns held in memory, like a spreadsheet...
Testing Pipeline Code with pytest
A function that looks right and a function that is verified right are different things.
Writing Production-Quality Pipeline Scripts
A script you run once in a notebook and a script that runs unattended every night need to be written differently.
Working with APIs
Calling an API with requests Pagination APIs return large result sets in pages.
Hands-On Lab
You will generate the acme-shop dataset, answer a revenue question two ways, write four small production-style scripts...
Quick Reference
Everyday patterns Pipeline habits
Common Mistakes
Mistakes that lose or duplicate data Advancing an incremental watermark before the write succeeds, or setting it to the...
Career Impact
Roles that use the skills in this module.
Data Engineer
Platform Engineer
DevOps Engineer
Site Reliability Engineer
Next Modules
Related Guides
Practice on the Coding Sheet
Not a software engineer sheet. Every problem comes from real DevOps, SRE, Platform and Cloud interviews, from your first script to a system you build yourself.
Open the Coding SheetFrequently Asked Questions
Python has connector libraries for almost every system, is quick to write and easy for a teammate to read later, and is what most data teams already use. It usually directs heavier engines like Spark or a warehouse rather than crunching rows itself.
A pipeline is idempotent when running it twice with the same input gives the same result as running it once. Schedulers retry failed jobs and teams re-run history on purpose, so a pipeline that appends duplicates on every run will quietly corrupt your data.
Logging adds timestamps and severity levels, and schedulers such as Airflow capture it automatically. Print output has no context and is easy to lose, which makes a failure at 2 AM very hard to trace.
A watermark is a saved marker, usually the latest updated_at value you processed. The next run asks the source only for records changed since that marker, so you stop re-downloading the whole dataset every day.