Running an ETL Pipeline

Ready to run your first ETL pipeline? Let's get to it!

Here, the functions extract(), transform(), and load() have been defined for you. To run this data ETL pipeline, you're going to execute each of these functions. If you're curious, take a peek at what the extract() function looks like.

def extract(file_name):
    print(f"Extracting data from {file_name}")
    return pd.read_csv(file_name)

This exercise is part of the course

ETL and ELT in Python

View Course

Exercise instructions

Use the extract() function to extract data from the raw_data.csv file.
Transform the extracted_data DataFrame using the transform() function.
Finally, load the transformed_data DataFrame to the cleaned_data SQL table.

Hands-on interactive exercise

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

# Extract data from the raw_data.csv file
extracted_data = ____(file_name="raw_data.csv")

# Transform the extracted_data
transformed_data = transform(data_frame=____)

# Load the transformed_data to cleaned_data.csv
____(data_frame=transformed_data, target_table="cleaned_data")

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