Building functions to extract data
It's important to modularize code when building a data pipeline. This helps to make pipelines more readable and reusable, and can help to expedite troubleshooting efforts. Creating and using functions for distinct operations in a pipeline can even help when getting started on a new project by providing a framework to begin development.
pandas has been imported as pd, and sqlalchemy is ready to be used.
This exercise is part of the course
ETL and ELT in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
def extract():
# Create a connection URI and connection engine
connection_uri = "postgresql+psycopg2://repl:password@localhost:____/____"
db_engine = sqlalchemy.____(connection_uri)