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

Deze oefening maakt deel uit van de cursus

ETL and ELT in Python

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Oefeninstructies

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

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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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