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Introductie tot Data Engineering

Vincent Vankrunkelsven

Data Engineer, DataCamp

De ETL-functie

def extract_table_to_df(tablename, db_engine):
  return pd.read_sql("SELECT * FROM {}".format(tablename), db_engine)


def split_columns_transform(df, column, pat, suffixes):
  # Zet kolom om naar str en splitst op pat...


def load_df_into_dwh(film_df, tablename, schema, db_engine):
  return film_df.to_sql(tablename, db_engine, schema=schema, if_exists="replace")


db_engines = { ... } # Moet worden geconfigureerd
def etl():
  # Extract
  film_df = extract_table_to_df("film", db_engines["store"])
  # Transform
  film_df = split_columns_transform(film_df, "rental_rate", ".", ["_dollar", "_cents"])
  # Load
  load_df_into_dwh(film_df, "film", "store", db_engines["dwh"])

Airflow-opfrisser

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Python
DAG's

Conceptueel voorbeeld van een DAG

Taken gedefinieerd met de task-decorator of met operators

Plannen met DAG's in Airflow

from airflow.sdk import dag

@dag(dag_id="sample", start_date=datetime(2024, 1, 1),
     schedule="0 0 * * *")
def sample():
    ...

# .------------------------- minuut           (0 - 59)
# | .----------------------- uur              (0 - 23)
# | | .--------------------- dag v/d maand    (1 - 31)
# | | | .------------------- maand            (1 - 12)
# | | | | .----------------- dag v/d week     (0 - 6)
# * * * * * <command>
0 * * * * # Elk uur op minuut 0

cf. https://crontab.guru

Het DAG-definitiebestand

from airflow.sdk import dag, task

@task(task_id="etl_task")
def etl():
    ...

@dag(dag_id="etl_pipeline",
     start_date=datetime(2024, 1, 1),
     schedule="0 0 * * *")
def etl_pipeline():
    wait_for_table = EmptyOperator(task_id="wait")
    wait_for_table >> etl()

etl_pipeline()

Het DAG-definitiebestand

from airflow.sdk import dag, task

...

etl_pipeline()

Opgeslagen als etl_dag.py in ~/airflow/dags/

Airflow-UI

Schermafbeelding van Airflow-UI

Laten we oefenen!

Introductie tot Data Engineering