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Introduktion till datatekniker

Vincent Vankrunkelsven

Data Engineer, DataCamp

ETL-funktionen

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):
  # Converts column into str and splits it on 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 = { ... } # Behöver konfigureras
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"])

Repetition av Airflow

Airflow-logotyp

Arbetsflödesschemaläggare
Python
DAG:er

Konceptuellt exempel på en DAG

Uppgifter definieras med dekoratorn task eller med operatorer

Schemaläggning med DAG:er i Airflow

from airflow.sdk import dag

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

# .------------------------- minute           (0 - 59)
# | .----------------------- hour             (0 - 23)
# | | .--------------------- day of the month (1 - 31)
# | | | .------------------- month            (1 - 12)
# | | | | .----------------- day of the week  (0 - 6)
# * * * * * <command>
0 * * * * # Varje timme vid minut 0

cf. https://crontab.guru

DAG-definitionsfilen

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()

DAG-definitionsfilen

from airflow.sdk import dag, task

...

etl_pipeline()

Sparad som etl_dag.py i ~/airflow/dags/

Airflow-gränssnittet

Skärmbild av Airflow-gränssnittet

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Introduktion till datatekniker