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

Latihan ini adalah bagian dari kursus

ETL and ELT in Python

Lihat Kursus

Petunjuk latihan

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

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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")

Edit dan Jalankan Kode