Apache Airflow is a platform developed by Airbnb in 2015 for programmatically authoring, scheduling, and monitoring workflows, known as DAGs (Directed Acyclic Graphs). It offers a flexible and user-friendly interface, supports various use cases like ETL pipelines and machine learning projects, and has a strong open-source community. While it has advantages such as task monitoring and extensibility, it also faces challenges with task optimization and flexibility in handling multiple tasks.

1. Presented By: Divyansh Jain
Software Consultant
Knoldus Inc
Apache Airflow

2. Agenda
What is Airflow?
How it works?
Pros & Cons
UI Screenshots
Demo
Why use Airflow ?

3. c
Airflow is a platform to
programmatically author, schedule
and monitor workflows (a.k.a DAGs or
Directed Acyclic Graph)
What is Apache
Airflow?

4. Incubating
- Created @ Airbnb in 2015 by Maxime
Beauchemin
- 6100+ Forks
- 16k+ Github Stars
- 1k+ Contributors
- 150+ companies officially using it
- More than 8k committers

5. What is Airflow?
Pipelines are configured as
code, allowing for dynamic
pipeline generation
A platform to schedule
data pipelines
Easy to define our own
operators, executors
It’s all about DAGs
A platform to monitor and
control data pipelines

6. Why to use Airflow ?
When would you use a Workflow Scheduler like
Airflow?
- ETL Pipelines
- Machine Learning Projects
- Predictive Data Pipelines such as Fraud Detection etc.
- Job Scheduling

7. Why to use Airflow ?
What should a Workflow do well?
- Schedule your workflow plan
- Handle task failures
- Monitor Performance

8. Very Flexible
- Rich User Interface
- Easily Extensible
- Allow communication between task
- Backfill Control
- Efficient CLI

9. Sample DAGs

10. How the Job initiates?
Web Server
Scheduler
Worker Worker
Metadata
User
- A user manages/ schedules
DAGs using Airflow
- Airflow webserver stores
scheduling metadata in
metadata DB
- Scheduler picks up new
schedule and distributes work
over Celery or RabbitMQ
- Workers picks up Airflow Task
Note- Celery is used to manage
node and Redis or RabbitMQ for
communication

11. Pros
Easy-to-use user interface - Independent scheduler Active open-source
community
● can easily view task logs
● can easily view hierarchy,
statuses, and code runs
● allows you to easily
change task statuses,
rerun historical tasks
● comes with its own
scheduler
● supports multiple DAGs
● chatroom itself is very
active
● newbies can get their
questions answered within
a span of few hours.

12. Cons
Task optimization - No direct dealing with
- tasks
Lesser flexibility
● sometimes unclear of
how to organize multiple
tasks into the massive
pipeline
● doesn't deal with data sets
or files as inputs of tasks
directly
● If database is lost, harder
to restore
● Workers cannot start the
task independently or pick
tasks flexibly.

13. DAG View

14. Tree View

15. Graph View

16. Code View

17. Thank You !