⛳️ 👩🏿‍🎓 👲🏽 Apache Airflow: ETL einfacher machen 🐶 🗿 👩🏼‍🍳

Hallo, ich bin Dmitry Logvinenko - Dateningenieur der Analytics-Abteilung der Lucky Group of Companies.

Ich werde Ihnen von einem großartigen Tool zur Entwicklung von ETL-Prozessen erzählen - Apache Airflow. Airflow ist jedoch so vielseitig und vielseitig, dass Sie es sich genauer ansehen sollten, auch wenn Sie sich nicht mit Datenströmen befassen, sondern regelmäßig Prozesse starten und deren Ausführung überwachen müssen.

Und ja, ich werde nicht nur erzählen, sondern auch zeigen: Das Programm enthält viel Code, Screenshots und Empfehlungen.

Was Sie normalerweise sehen, wenn Sie das Wort Airflow / Wikimedia Commons googeln

Inhaltsverzeichnis

Einführung

Apache Airflow ist genau wie Django:

geschrieben in Python,
Es gibt ein ausgezeichnetes Admin-Panel.
unbegrenzt erweiterbar,

- nur besser und für ganz andere Zwecke gemacht, nämlich (wie vor Kata geschrieben):

( Celery/Kubernetes )
workflow Python-
API , ( ).

Apache Airflow :

( SQL Server PostgreSQL, API , 1) DWH ODS ( Vertica Clickhouse).
cron, ODS, .

32 50 GB . Airflow :

200 ( workflows, ),
70 ,
( ) .

, , , über-, :

SQL Server’, 50 — , , ( , --), Orders ( ). , (-, -, ETL-) , , Vertica.

, ( )

( )

, SQL- , ETL- aka :

Informatica Power Center — , , , . 1% . ? , -, - . -, , ---. , Airbus A380/, .

, 30

SQL Server Integration Server — . : SQL Server , ETL- - . : , … , . dtsx ( XML ) , ? , ? , , . , , :

. SSIS-...

… . Apache Airflow.

, ETL- — Python-, . , Python- - 13” .

, , Airflow, , Celery , .

, docker-compose.yml :

Airflow: Scheduler, Webserver. Flower Celery- ( apache/airflow:1.10.10-python3.7, );
PostgreSQL, Airflow ( , . .), Celery — ;
Redis, Celery;
Celery worker, .
./dags . , .

- ( ), - . https://github.com/dm-logv/airflow-tutorial.

docker-compose.yml

version: '3.4'

x-airflow-config: &airflow-config
  AIRFLOW__CORE__DAGS_FOLDER: /dags
  AIRFLOW__CORE__EXECUTOR: CeleryExecutor
  AIRFLOW__CORE__FERNET_KEY: MJNz36Q8222VOQhBOmBROFrmeSxNOgTCMaVp2_HOtE0=
  AIRFLOW__CORE__HOSTNAME_CALLABLE: airflow.utils.net:get_host_ip_address
  AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgres+psycopg2://airflow:airflow@airflow-db:5432/airflow

  AIRFLOW__CORE__PARALLELISM: 128
  AIRFLOW__CORE__DAG_CONCURRENCY: 16
  AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG: 4
  AIRFLOW__CORE__LOAD_EXAMPLES: 'False'
  AIRFLOW__CORE__LOAD_DEFAULT_CONNECTIONS: 'False'

  AIRFLOW__EMAIL__DEFAULT_EMAIL_ON_RETRY: 'False'
  AIRFLOW__EMAIL__DEFAULT_EMAIL_ON_FAILURE: 'False'

  AIRFLOW__CELERY__BROKER_URL: redis://broker:6379/0
  AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@airflow-db/airflow

x-airflow-base: &airflow-base
  image: apache/airflow:1.10.10-python3.7
  entrypoint: /bin/bash
  restart: always
  volumes:
    - ./dags:/dags
    - ./requirements.txt:/requirements.txt

services:
  # Redis as a Celery broker
  broker:
    image: redis:6.0.5-alpine

  # DB for the Airflow metadata
  airflow-db:
    image: postgres:10.13-alpine

    environment:
      - POSTGRES_USER=airflow
      - POSTGRES_PASSWORD=airflow
      - POSTGRES_DB=airflow

    volumes:
      - ./db:/var/lib/postgresql/data

  # Main container with Airflow Webserver, Scheduler, Celery Flower
  airflow:
    <<: *airflow-base

    environment:
      <<: *airflow-config

      AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL: 30
      AIRFLOW__SCHEDULER__CATCHUP_BY_DEFAULT: 'False'
      AIRFLOW__SCHEDULER__MAX_THREADS: 8

      AIRFLOW__WEBSERVER__LOG_FETCH_TIMEOUT_SEC: 10

    depends_on:
      - airflow-db
      - broker

    command: >
      -c " sleep 10 &&
           pip install --user -r /requirements.txt &&
           /entrypoint initdb &&
          (/entrypoint webserver &) &&
          (/entrypoint flower &) &&
           /entrypoint scheduler"

    ports:
      # Celery Flower
      - 5555:5555
      # Airflow Webserver
      - 8080:8080

  # Celery worker, will be scaled using `--scale=n`
  worker:
    <<: *airflow-base

    environment:
      <<: *airflow-config

    command: >
      -c " sleep 10 &&
           pip install --user -r /requirements.txt &&
           /entrypoint worker"

    depends_on:
      - airflow
      - airflow-db
      - broker

puckel/docker-airflow – . , .
Airflow airflow.cfg, ( ), .
, production-ready: heartbeats , . , .
, :
- , .
- — .

$ docker-compose up --scale worker=3

, , -:

Airflow: http://127.0.0.1:8080/admin/
Flower: http://127.0.0.1:5555/dashboard

«», :

Scheduler — Airflow, , , : , , .

, , (, , ) - run_duration — . .
DAG ( «») — « », , (. ) Package SSIS Workflow Informatica.

, .
DAG Run — , execution_date. ( , , ).
Operator — , - . :
- action, PythonOperator, () Python-;
- transfer, , , MsSqlToHiveTransfer;
- sensor - . HttpSensor , , GoogleCloudStorageToS3Operator. : «? !» , . , .
Task — .
Task instance — - , - ( , LocalExecutor CeleryExecutor), (. . — ), .

, , .

, :

from datetime import timedelta, datetime

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

from commons.datasources import sql_server_ds

dag = DAG('orders',
          schedule_interval=timedelta(hours=6),
          start_date=datetime(2020, 7, 8, 0))

def workflow(**context):
    print(context)

for conn_id, schema in sql_server_ds:
    PythonOperator(
        task_id=schema,
        python_callable=workflow,
        provide_context=True,
        dag=dag)

;
sql_server_ds — List[namedtuple[str, str]] Airflow Connections ;
dag — , globals(), Airflow . :
- orders — -,
- , ,
- , 6 ( timedelta() cron- 0 0 0/6 ? * * *, — @daily);
workflow() , . .
:
- ;
- PythonOperator, workflow(). ( ) . provide_context , **context.

. :

-,
, ( Airflow, Celery ).

, .

docker-compose.yml requirements.txt .

— task instances, .

, :

, , — . — .

, ./dags, — git Gitlab, Gitlab CI master.

Flower

-, , - — Flower.

, :

— :

, , .

— . Airflow , .

task instances.

, :

, Clear . , , - , .

, — Airflow. , : Browse/Task Instances

( , ):

,

DAG, update_reports.py:

from collections import namedtuple
from datetime import datetime, timedelta
from textwrap import dedent

from airflow import DAG
from airflow.contrib.operators.vertica_operator import VerticaOperator
from airflow.operators.email_operator import EmailOperator
from airflow.utils.trigger_rule import TriggerRule

from commons.operators import TelegramBotSendMessage

dag = DAG('update_reports',
          start_date=datetime(2020, 6, 7, 6),
          schedule_interval=timedelta(days=1),
          default_args={'retries': 3, 'retry_delay': timedelta(seconds=10)})

Report = namedtuple('Report', 'source target')
reports = [Report(f'{table}_view', table) for table in [
    'reports.city_orders',
    'reports.client_calls',
    'reports.client_rates',
    'reports.daily_orders',
    'reports.order_duration']]

email = EmailOperator(
    task_id='email_success', dag=dag,
    to='{{ var.value.all_the_kings_men }}',
    subject='DWH Reports updated',
    html_content=dedent(""" ,  """),
    trigger_rule=TriggerRule.ALL_SUCCESS)

tg = TelegramBotSendMessage(
    task_id='telegram_fail', dag=dag,
    tg_bot_conn_id='tg_main',
    chat_id='{{ var.value.failures_chat }}',
    message=dedent("""\
         , ,  {{ dag.dag_id }} 
        """),
    trigger_rule=TriggerRule.ONE_FAILED)

for source, target in reports:
    queries = [f"TRUNCATE TABLE {target}",
               f"INSERT INTO {target} SELECT * FROM {source}"]

    report_update = VerticaOperator(
        task_id=target.replace('reports.', ''),
        sql=queries, vertica_conn_id='dwh',
        task_concurrency=1, dag=dag)

    report_update >> [email, tg]

- ? : , ; , ; , ( , ).

from commons.operators import TelegramBotSendMessage — , , . ( );
default_args={} — ;
to='{{ var.value.all_the_kings_men }}' — to , Jinja email-, Admin/Variables;
trigger_rule=TriggerRule.ALL_SUCCESS — . , ;
tg_bot_conn_id='tg_main' — conn_id , Admin/Connections;
trigger_rule=TriggerRule.ONE_FAILED — Telegram ;
task_concurrency=1 — task instances . , VerticaOperator ( );
report_update >> [email, tg] — VerticaOperator , :

- , . Tree View :

— .

— Jinja-, . , :

SELECT
    id,
    payment_dtm,
    payment_type,
    client_id
FROM orders.payments
WHERE
    payment_dtm::DATE = '{{ ds }}'::DATE

{{ ds }} execution_date YYYY-MM-DD: 2020-07-14. , ( Tree View), .

Rendered -. :

: Macros Reference

, , , .

, ( ). Admin/Variables :

, :

TelegramBotSendMessage(chat_id='{{ var.value.failures_chat }}')

, JSON. JSON-:

bot_config

{
    "bot": {
        "token": 881hskdfASDA16641,
        "name": "Verter"
    },
    "service": "TG"
}

: {{ var.json.bot_config.bot.token }}.

. : Admin/Connections , / . :

( , ), ( tg_main) — , Airflow ( - — ), .

: BaseHook.get_connection(), , ( Round Robin, Airflow).

Variables Connections, , , : , — Airflow. C , , , UI. — - , () .

— . Airflow — . , JiraHook Jira ( -), SambaHook smb-.

, , TelegramBotSendMessage

commons/operators.py :

from typing import Union

from airflow.operators import BaseOperator

from commons.hooks import TelegramBotHook, TelegramBot

class TelegramBotSendMessage(BaseOperator):
    """Send message to chat_id using TelegramBotHook

    Example:
        >>> TelegramBotSendMessage(
        ...     task_id='telegram_fail', dag=dag,
        ...     tg_bot_conn_id='tg_bot_default',
        ...     chat_id='{{ var.value.all_the_young_dudes_chat }}',
        ...     message='{{ dag.dag_id }} failed :(',
        ...     trigger_rule=TriggerRule.ONE_FAILED)
    """
    template_fields = ['chat_id', 'message']

    def __init__(self,
                 chat_id: Union[int, str],
                 message: str,
                 tg_bot_conn_id: str = 'tg_bot_default',
                 *args, **kwargs):
        super().__init__(*args, **kwargs)

        self._hook = TelegramBotHook(tg_bot_conn_id)
        self.client: TelegramBot = self._hook.client
        self.chat_id = chat_id
        self.message = message

    def execute(self, context):
        print(f'Send "{self.message}" to the chat {self.chat_id}')
        self.client.send_message(chat_id=self.chat_id,
                                 message=self.message)

, Airflow, :

BaseOperator, Airflow- ( )
template_fields, Jinja .
__init__(), , .
.
TelegramBotHook, -.
() BaseOperator.execute(), Airfow , — , . (, , stdout stderr — Airflow , , , .)

, commons/hooks.py. , :

from typing import Union

from airflow.hooks.base_hook import BaseHook
from requests_toolbelt.sessions import BaseUrlSession

class TelegramBotHook(BaseHook):
    """Telegram Bot API hook

    Note: add a connection with empty Conn Type and don't forget
    to fill Extra:

        {"bot_token": "YOuRAwEsomeBOtToKen"}
    """
    def __init__(self,
                 tg_bot_conn_id='tg_bot_default'):
        super().__init__(tg_bot_conn_id)

        self.tg_bot_conn_id = tg_bot_conn_id
        self.tg_bot_token = None
        self.client = None
        self.get_conn()

    def get_conn(self):
        extra = self.get_connection(self.tg_bot_conn_id).extra_dejson
        self.tg_bot_token = extra['bot_token']
        self.client = TelegramBot(self.tg_bot_token)
        return self.client

, , :

, — : conn_id;
: get_conn(), - extra ( JSON), ( !) Telegram-: {"bot_token": "YOuRAwEsomeBOtToKen"}.
TelegramBot, .

. c TelegramBotHook().clent TelegramBotHook().get_conn().

, Telegram REST API, python-telegram-bot sendMessage.

class TelegramBot:
    """Telegram Bot API wrapper

    Examples:
        >>> TelegramBot('YOuRAwEsomeBOtToKen', '@myprettydebugchat').send_message('Hi, darling')
        >>> TelegramBot('YOuRAwEsomeBOtToKen').send_message('Hi, darling', chat_id=-1762374628374)
    """
    API_ENDPOINT = 'https://api.telegram.org/bot{}/'

    def __init__(self, tg_bot_token: str, chat_id: Union[int, str] = None):
        self._base_url = TelegramBot.API_ENDPOINT.format(tg_bot_token)
        self.session = BaseUrlSession(self._base_url)
        self.chat_id = chat_id

    def send_message(self, message: str, chat_id: Union[int, str] = None):
        method = 'sendMessage'

        payload = {'chat_id': chat_id or self.chat_id,
                   'text': message,
                   'parse_mode': 'MarkdownV2'}

        response = self.session.post(method, data=payload).json()
        if not response.get('ok'):
            raise TelegramBotException(response)

class TelegramBotException(Exception):
    def __init__(self, *args, **kwargs):
        super().__init__((args, kwargs))

— : TelegramBotSendMessage, TelegramBotHook, TelegramBot — , , Open Source.

, . , ...

- ! ? !

- ?

, - ? SQL Server Vertica , , !

, - . .

,
SQL Server
Vertica

, , docker-compose.yml:

docker-compose.db.yml

version: '3.4'

x-mssql-base: &mssql-base
  image: mcr.microsoft.com/mssql/server:2017-CU21-ubuntu-16.04
  restart: always
  environment:
    ACCEPT_EULA: Y
    MSSQL_PID: Express
    SA_PASSWORD: SayThanksToSatiaAt2020
    MSSQL_MEMORY_LIMIT_MB: 1024

services:
  dwh:
    image: jbfavre/vertica:9.2.0-7_ubuntu-16.04

  mssql_0:
    <<: *mssql-base

  mssql_1:
    <<: *mssql-base

  mssql_2:
    <<: *mssql-base

  mssql_init:
    image: mio101/py3-sql-db-client-base
    command: python3 ./mssql_init.py
    depends_on:
      - mssql_0
      - mssql_1
      - mssql_2
    environment:
      SA_PASSWORD: SayThanksToSatiaAt2020
    volumes:
      - ./mssql_init.py:/mssql_init.py
      - ./dags/commons/datasources.py:/commons/datasources.py

Vertica dwh ,
SQL Server,
- ( mssql_init.py!)

, , :

$ docker-compose -f docker-compose.yml -f docker-compose.db.yml up --scale worker=3

, , Data Profiling/Ad Hoc Query:

ETL- , : , , , :

with Session(task_name) as session:
    print('Load', session.id, 'started')

    # Load worflow
    ...

    session.successful = True
    session.loaded_rows = 15

session.py

from sys import stderr

class Session:
    """ETL workflow session

    Example:
        with Session(task_name) as session:
            print(session.id)
            session.successful = True
            session.loaded_rows = 15
            session.comment = 'Well done'
    """

    def __init__(self, connection, task_name):
        self.connection = connection
        self.connection.autocommit = True

        self._task_name = task_name
        self._id = None

        self.loaded_rows = None
        self.successful = None
        self.comment = None

    def __enter__(self):
        return self.open()

    def __exit__(self, exc_type, exc_val, exc_tb):
        if any(exc_type, exc_val, exc_tb):
            self.successful = False
            self.comment = f'{exc_type}: {exc_val}\n{exc_tb}'
            print(exc_type, exc_val, exc_tb, file=stderr)
        self.close()

    def __repr__(self):
        return (f'<{self.__class__.__name__} ' 
                f'id={self.id} ' 
                f'task_name="{self.task_name}">')

    @property
    def task_name(self):
        return self._task_name

    @property
    def id(self):
        return self._id

    def _execute(self, query, *args):
        with self.connection.cursor() as cursor:
            cursor.execute(query, args)
            return cursor.fetchone()[0]

    def _create(self):
        query = """
            CREATE TABLE IF NOT EXISTS sessions (
                id          SERIAL       NOT NULL PRIMARY KEY,
                task_name   VARCHAR(200) NOT NULL,

                started     TIMESTAMPTZ  NOT NULL DEFAULT current_timestamp,
                finished    TIMESTAMPTZ           DEFAULT current_timestamp,
                successful  BOOL,

                loaded_rows INT,
                comment     VARCHAR(500)
            );
            """
        self._execute(query)

    def open(self):
        query = """
            INSERT INTO sessions (task_name, finished)
            VALUES (%s, NULL)
            RETURNING id;
            """
        self._id = self._execute(query, self.task_name)
        print(self, 'opened')
        return self

    def close(self):
        if not self._id:
            raise SessionClosedError('Session is not open')
        query = """
            UPDATE sessions
            SET
                finished    = DEFAULT,
                successful  = %s,
                loaded_rows = %s,
                comment     = %s
            WHERE
                id = %s
            RETURNING id;
            """
        self._execute(query, self.successful, self.loaded_rows,
                      self.comment, self.id)
        print(self, 'closed',
              ', successful: ', self.successful,
              ', Loaded: ', self.loaded_rows,
              ', comment:', self.comment)

class SessionError(Exception):
    pass

class SessionClosedError(SessionError):
    pass

. :

source_conn = MsSqlHook(mssql_conn_id=src_conn_id, schema=src_schema).get_conn()

query = f"""
    SELECT 
        id, start_time, end_time, type, data
    FROM dbo.Orders
    WHERE
        CONVERT(DATE, start_time) = '{dt}'
    """

df = pd.read_sql_query(query, source_conn)

Airflow pymssql-
— .
pandas, DataFrame — .

{dt} %s , , pandas pymssql params: List, tuple.

, pymssql , pyodbc.

, Airflow :

, . . . --, ?! :

if df.empty:
    raise AirflowSkipException('No rows to load')

AirflowSkipException Airflow, , , . , pink.

df['etl_source'] = src_schema
df['etl_id'] = session.id
df['hash_id'] = hash_pandas_object(df[['etl_source', 'id']])

, ,
( ),
— ( ) .

: Vertica. , , — CSV!

# Export data to CSV buffer
buffer = StringIO()
df.to_csv(buffer,
          index=False, sep='|', na_rep='NUL', quoting=csv.QUOTE_MINIMAL,
          header=False, float_format='%.8f', doublequote=False, escapechar='\\')
buffer.seek(0)

# Push CSV
target_conn = VerticaHook(vertica_conn_id=target_conn_id).get_conn()

copy_stmt = f"""
    COPY {target_table}({df.columns.to_list()}) 
    FROM STDIN 
    DELIMITER '|' 
    ENCLOSED '"' 
    ABORT ON ERROR 
    NULL 'NUL'
    """

cursor = target_conn.cursor()
cursor.copy(copy_stmt, buffer)

StringIO.
pandas DataFrame CSV-.
Vertica .
copy() !

, , , :

session.loaded_rows = cursor.rowcount
session.successful = True

. :

create_schema_query = f'CREATE SCHEMA IF NOT EXISTS {target_schema};'
create_table_query = f"""
    CREATE TABLE IF NOT EXISTS {target_schema}.{target_table} (
         id         INT,
         start_time TIMESTAMP,
         end_time   TIMESTAMP,
         type       INT,
         data       VARCHAR(32),
         etl_source VARCHAR(200),
         etl_id     INT,
         hash_id    INT PRIMARY KEY
     );"""

create_table = VerticaOperator(
    task_id='create_target',
    sql=[create_schema_query,
         create_table_query],
    vertica_conn_id=target_conn_id,
    task_concurrency=1,
    dag=dag)

VerticaOperator() ( , ). , :

for conn_id, schema in sql_server_ds:
    load = PythonOperator(
        task_id=schema,
        python_callable=workflow,
        op_kwargs={
            'src_conn_id': conn_id,
            'src_schema': schema,
            'dt': '{{ ds }}',
            'target_conn_id': target_conn_id,
            'target_table': f'{target_schema}.{target_table}'},
        dag=dag)

    create_table >> load

— , — , — ,

, ?

, «»

, : ETL-: SSIS Airflow… … , , , !

- , Apache Airflow — — .

: , — Airflow : , , ( , ).

, -

,

start_date. , . start_date . , start_date , schedule_interval — , DAG .
```
start_date = datetime(2020, 7, 7, 0, 1, 2)
```
.

: Task is missing the start_date parameter, , .
. , ( Airflow ), -, , . . , PostgreSQL 20 5 , .
LocalExecutor. , , . LocalExecutor’ , , , CeleryExecutor. , , Celery , «, , !»
:
- Connections ,
- SLA Misses , ,
- XCom ( !) .
. ? . Gmail >90k Airflow, - 100 .

: Apache Airflow Pitfails

, , Airflow :

REST API — Experimental, . , / , DAG Run .
CLI — , WebUI, . :
- backfill .
  
  , , : « , , 1 13 ! ---!». :
```
airflow backfill -s '2020-01-01' -e '2020-01-13' orders
```
- : initdb, resetdb, upgradedb, checkdb.
- run, , . , LocalExecutor, Celery-.
- test, .
- connections .

Python API — , , . /home/airflow/dags, ipython ? , , :

from airflow import settings
from airflow.models import Connection

fields = 'conn_id conn_type host port schema login password extra'.split()

session = settings.Session()
for conn in session.query(Connection).order_by(Connection.conn_id):
  d = {field: getattr(conn, field) for field in fields}
  print(conn.conn_id, '=', d)

Airflow. , , API.

, , . — , .

, SQL!

WITH last_executions AS (
SELECT
    task_id,
    dag_id,
    execution_date,
    state,
        row_number()
        OVER (
            PARTITION BY task_id, dag_id
            ORDER BY execution_date DESC) AS rn
FROM public.task_instance
WHERE
    execution_date > now() - INTERVAL '2' DAY
),
failed AS (
    SELECT
        task_id,
        dag_id,
        execution_date,
        state,
        CASE WHEN rn = row_number() OVER (
            PARTITION BY task_id, dag_id
            ORDER BY execution_date DESC)
                 THEN TRUE END AS last_fail_seq
    FROM last_executions
    WHERE
        state IN ('failed', 'up_for_retry')
)
SELECT
    task_id,
    dag_id,
    count(last_fail_seq)                       AS unsuccessful,
    count(CASE WHEN last_fail_seq
        AND state = 'failed' THEN 1 END)       AS failed,
    count(CASE WHEN last_fail_seq
        AND state = 'up_for_retry' THEN 1 END) AS up_for_retry
FROM failed
GROUP BY
    task_id,
    dag_id
HAVING
    count(last_fail_seq) > 0

Airflow .

Apache Airflow Documentation — , . , ?
Best Practices — .
The Airflow UI — :
Understanding Apache Airflow’s key concepts — , (!) - .
Tianlong's Blog — A Guide On How To Build An Airflow Server/Cluster — Airflow-.
Running Apache Airflow At Lyft — , , .
How Apache Airflow Distributes Jobs on Celery workers — Celery.
DAG Writing Best Practices in Apache Airflow — , ID , , .
Managing Dependencies in Apache Airflow — Trigger Rule, .
Airflow: When Your DAG is Far Behind The Schedule — «, » , .
Useful SQL queries for Apache Airflow — SQL- Airflow.
Get started developing workflows with Apache Airflow — .
Building the Fetchr Data Science Infra on AWS with Presto and Airflow — AWS Data Science.
7 Common Errors to Check when Debugging Airflow DAGs — ( - - ).
Store and access password using Apache Airflow — , , Connections.
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Apache Airflow: ETL einfacher machen

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Einführung

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