The "T" in ELT

Let's not forget about ELT! Here, the extract() and load() functions have been defined for you. Now, all that's left is to finish defining the transform() function and run the pipeline. Go get 'em!

This exercise is part of the course

ETL and ELT in Python

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Exercise instructions

Update the transform() function to call the .execute() method on the data_warehouse object.
Use the newly-updated transform() function to populate data in the total_sales target table by transforming data in the raw_sales_data source table.

Hands-on interactive exercise

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

# Complete building the transform() function
def transform(source_table, target_table):
  data_warehouse.____(f"""
  CREATE TABLE {target_table} AS
      SELECT
          CONCAT("Product ID: ", product_id),
          quantity * price
      FROM {source_table};
  """)

extracted_data = extract(file_name="raw_sales_data.csv")
load(data_frame=extracted_data, table_name="raw_sales_data")

# Populate total_sales by transforming raw_sales_data
____(source_table="____", target_table="____")

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