1. Learn
    2. /
  2. Courses
    3. /
  3. ETL and ELT in Python
Connected

Exercise

Data pipeline architecture patterns

When building data pipelines, it's best to separate the files where functions are being defined from where they are being run.

In this exercise, you'll practice importing components of a pipeline into memory before using these functions to run the pipeline end-to-end. The project takes the following format, where pipeline_utils stores the extract(), transform(), and load() functions that will be used run the pipeline.

> ls
 etl_pipeline.py
 pipeline_utils.py

Instructions

100 XP
  • Import the extract, transform, and load functions from the pipeline_utils module.
  • Use the functions imported to run the data pipeline end-to-end.