Apache Airflow is an open-source workflow management platform designed for authoring, scheduling, and monitoring data pipelines via directed acyclic graphs (DAGs). Its modular architecture consists of a web server, scheduler, workers, and a metadata database, with capabilities for extensibility and integration with various data sources. Airflow is valuable for managing complex workflows, promoting code reusability, and ensuring reliable execution in data engineering and ETL processes.

1. Apache Airflow
Data Pipelines Made Easy

2. Table of contents
01
What is Airflow?
Introduction to Apache
Airflow
02
Airflow Architecture
Understanding Airflow's
Components
03
DAGs and Tasks
Building Blocks of Airflow
Pipelines
04
Scheduling and
Monitoring
Managing Airflow Workflows
05
Extensibility and
Integrations
Customizing Airflow for Your
Needs
06
Use Cases and
Benefits
Why Airflow is Valuable

3. 1
What is Airflow?
Introduction to Apache Airflow

4. What is Airflow?
• Apache Airflow is an open-source workflow
management platform.
• It helps to author, schedule, and monitor
data pipelines.
• Airflow allows you to define your workflows
as Directed Acyclic Graphs (DAGs).
• It is written in Python and is highly
extensible.

5. 2
Airflow Architecture
Understanding Airflow's Components

6. Airflow Architecture
• Airflow has a modular architecture with four
main components:
- Web Server: User interface to monitor and
trigger workflows
- Scheduler: Schedules and monitors DAG
executions
- Workers: Execute tasks defined in the DAGs
- Metadata Database: Stores DAG definitions
and execution history

7. 3
DAGs and Tasks
Building Blocks of Airflow Pipelines

8. DAGs and Tasks
• DAGs (Directed Acyclic Graphs) define the
workflow as a collection of tasks.
• Tasks are individual units of work, such as
data transformations or API calls.
• Tasks can have dependencies on other tasks,
defining the execution order.
• Airflow provides many built-in operators for
common tasks.

9. 4
Scheduling and Monitoring
Managing Airflow Workflows

10. Scheduling and Monitoring
• Airflow allows you to schedule DAGs to run
at specific intervals or times.
• You can monitor the status of DAG runs and
individual tasks through the Web UI.
• Airflow provides detailed logs and metrics for
troubleshooting and performance analysis.
• Alerts can be set up to notify you of failures
or SLA breaches.

11. 5
Extensibility and Integrations
Customizing Airflow for Your Needs

12. Extensibility and Integrations
• Airflow is highly extensible, allowing you to
create custom operators and plugins.
• It integrates with various data sources,
processing engines, and cloud platforms.
• Airflow has a growing ecosystem of providers
and third-party integrations.
• You can extend Airflow's functionality with
custom hooks, sensors, and executors.

13. 6
Use Cases and Benefits
Why Airflow is Valuable

14. Use Cases and Benefits
• Airflow is widely used for data engineering
and ETL pipelines.
• It simplifies the management of complex,
interdependent workflows.
• Airflow promotes code reusability and
collaboration.
• It provides visibility and control over your
data pipelines.
• Airflow is scalable and fault-tolerant,
ensuring reliable execution.