Scheduling daily jobs

Introduction to Data Engineering

Vincent Vankrunkelsven

Data Engineer @ DataCamp

What you've done so far

Extract using extract_course_data() and extract_rating_data()
Clean up using NA using transform_fill_programming_language()
Average course ratings per course: transform_avg_rating()
Get eligible user and course id pairs: transform_courses_to_recommend()
Calculate the recommendations: transform_recommendations()

Loading to Postgres

Use the calculations in data products
Update daily
Example use case: sending out e-mails with recommendations

The loading phase

recommendations.to_sql(
    "recommendations",
    db_engine,
    if_exists="append",
)

def etl(db_engines):
    # Extract the data
    courses = extract_course_data(db_engines)
    rating = extract_rating_data(db_engines)
    # Clean up courses data
    courses = transform_fill_programming_language(courses)

    # Get the average course ratings
    avg_course_rating = transform_avg_rating(rating)

    # Get eligible user and course id pairs
    courses_to_recommend = transform_courses_to_recommend(
        rating,
        courses,
    )

    # Calculate the recommendations
    recommendations = transform_recommendations(
        avg_course_rating,
        courses_to_recommend,
    )

    # Load the recommendations into the database
    load_to_dwh(recommendations, db_engine))

Creating the DAG

from airflow.models import DAG
from airflow.operators.python_operator import PythonOperator

dag = DAG(dag_id="recommendations",
          scheduled_interval="0 0 * * *")

task_recommendations = PythonOperator(
    task_id="recommendations_task",
    python_callable=etl,
)

Let's practice!

Introduction to Data Engineering