Programar tareas diarias

Introducción a la ingeniería de datos

Vincent Vankrunkelsven

Data Engineer @ DataCamp

Lo que has hecho hasta ahora

Extrae con extract_course_data() y extract_rating_data()
Rellena NAs con transform_fill_programming_language()
Promedia valoraciones por curso: transform_avg_rating()
Obtén pares válidos usuario–curso: transform_courses_to_recommend()
Calcula recomendaciones: transform_recommendations()

Cargar en Postgres

Usa los cálculos en data products
Actualiza a diario
Caso de uso: enviar emails con recomendaciones

Fase de carga

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))

Crear el 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,
)

¡Vamos a practicar!

Introducción a la ingeniería de datos