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

This exercise is part of the course

ETL and ELT in Python

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Exercise instructions

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

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import the extract, transform, and load functions from pipeline_utils
____

# Run the pipeline end to end by extracting, transforming and loading the data
raw_tax_data = ____("raw_tax_data.csv")
clean_tax_data = ____(raw_tax_data)
____(clean_tax_data, "clean_tax_data.parquet")

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