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Apache Airflow

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Knoldus Inc.
Knoldus Inc.

In the session, we discussed the End-to-end working of Apache Airflow that mainly focused on "Why What and How" factors. It includes the DAG creation/implementation, Architecture, pros & cons. It also includes how the DAG is created for scheduling the Job and what all steps are required to create the DAG using python script & finally with the working demo.

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Presented By: Divyansh Jain Software Consultant Knoldus Inc Apache Airflow
Agenda What is Airflow? How it works? Pros & Cons UI Screenshots Demo Why use Airflow ?
c Airflow is a platform to programmatically author, schedule and monitor workflows (a.k.a DAGs or Directed Acyclic Graph) What is Apache Airflow?
Incubating - Created @ Airbnb in 2015 by Maxime Beauchemin - 6100+ Forks - 16k+ Github Stars - 1k+ Contributors - 150+ companies officially using it - More than 8k committers
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
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
Why to use Airflow ? What should a Workflow do well? - Schedule your workflow plan - Handle task failures - Monitor Performance
Very Flexible - Rich User Interface - Easily Extensible - Allow communication between task - Backfill Control - Efficient CLI
Sample DAGs
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
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.
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.
DAG View
Tree View
Graph View
Code View
Thank You !

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Apache Airflow

  • 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
  • 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.
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