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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!

Este ejercicio forma parte del curso

Introducción a las canalizaciones de datos

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Instrucciones de ejercicio

  • 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.

Ejercicio interactivo práctico

Pruebe este ejercicio completando este código de muestra.

# 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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