ELT in Action

Feeling pretty good about running ETL processes? Well, it's time to give ELT pipelines a try. Like before, the extract(), load(), and transform() functions have been defined for you; all you'll have to worry about is running these functions. Good luck!

This exercise is part of the course

ETL and ELT in Python

View Course

Exercise instructions

Use the appropriate ETL function to extract data from the raw_data.csv file.
Load the raw_data DataFrame into the raw_data table in a data warehouse.
Call the transform() function to transform the data in the raw_data source table.

Hands-on interactive exercise

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

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

# Load the extracted_data to the raw_data table
load(data_frame=____, table_name="____")

# Transform data in the raw_data table
____(
  source_table="____", 
  target_table="cleaned_data"
)

Edit and Run Code