Apache Airflow is an open-source platform built for orchestrating and managing workflows using Python. Its versatility allows users to define complex pipelines through Python scripts, incorporating loops, shell commands, and external modules like pandas, scikit-learn, and cloud service libraries (GCP, AWS).

Many organizations trust Airflow for its dependability:

Pinterest: Overcame overall performance and scalability issues, lowering maintenance costs.

GoDaddy: Supports batch analytics and records teams with an orchestration device and pre-built operators for ETL pipelines.

DXC Technology: Implemented Airflow to manage an undertaking with massive facts storage desires, presenting a stable orchestration engine.

APACHE AIRFLOW USE CASES
Key Use Cases:

ETL Pipelines: Extracting data from more than one assets, walking Spark jobs, and appearing records modifications.
Machine Learning Models: Training and deploying models.
Report Generation: Automating document creation.
Backups and DevOps Operations: Automating backup methods and comparable tasks.
Additionally, Airflow supports advert hoc workloads and may be manually induced via REST API, demonstrating its flexibility and programmability using Python.

Core Concepts of Apache Airflow 2.0

Airflow DAG (Directed Acyclic Graph)

Definition: Workflows in Airflow are defined the use of DAGs, which might be Python files.
Unique Identification: Each DAG is diagnosed by a unique dag_id.
Scheduling:
schedule_interval: Defines while the DAG need to run (e.g. timedelta (days=2), cron expressions, or None for manual/external triggers).
Start_date: The date from which the DAG starts running (the usage of days_ago is not unusual).

from airflow.models import DAG
from airflow.utils.dates import days_ago
with DAG(
"etl_sales_daily",
start_date=days_ago(1),
schedule_interval=None,
) as dag:
...

Adding Tasks to a DAG

Operators: Tasks are defined the use of operators and have specific task_ids in the DAG.
Dependencies:

• Upstream duties: Tasks performed before the present day task.
• Downstream duties: Tasks performed after the cutting-edge challenge.
• Example:

from airflow.operators.dummy_operator import DummyOperator
task_a = DummyOperator(task_id="task_a")
task_b = DummyOperator(task_id="task_b")
task_c = DummyOperator(task_id="task_c")
task_d = DummyOperator(task_id="task_d")
task_a >> [task_b, task_c]
task_c >> task_d

*[Good Read: Comparison between Mydumper, mysqldump, xtrabackup]
*
Graphic representation: A visual representation of the DAG showing a dependent task.

Trigger rules

all_success: All upstream operations must succeed. one_success: At least one top job must succeed. none_failed: No upstream task can fail (successful or just skipped).

Airflow can simplify the interpretation of complex business processes, through operator, dependency, and trigger rules.

You can check more info about:Building and Managing Production-Ready Apache Airflow.

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