Apache Airflow

Apache Airflow

Apache Airflow is an open-source workflow management platform used to programmatically author, schedule and monitor workflows. It provides a graphical interface to visualize pipelines and integrates with databases and other environments.
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scheduling pipelines workflows data-pipelines etl

Apache Airflow: Open-Source Workflow Management Platform

Apache Airflow is an open-source workflow management platform used to programmatically author, schedule and monitor workflows. It provides a graphical interface to visualize pipelines and integrates with databases and other environments.

What is Apache Airflow?

Apache Airflow is an open-source workflow management platform created by Airbnb in 2015. It is used to programmatically author, schedule and monitor workflows. Airflow provides a graphical interface to visualize pipelines, dependencies between tasks, and monitor the workflow.

Some key features and benefits of Apache Airflow include:

  • Directed Acyclic Graphs (DAGs) - Allows you to author workflows as code. The pipelines are defined as Python code making them portable and versionable.
  • Extensibility - It has an open plugin framework allowing you to customize it for different environments. There are also hundreds of community contributed operators and hooks.
  • Scalability - Airflow leverages the computing capabilities of the underlying infrastructure. It can handle high volume, frequency, complexity, and variety of tasks.
  • Portability - Airflow pipelines are defined via code making the environment agnostic. You can run it on-premise or on any cloud provider.
  • Monitoring - Built-in interfaces to track status of tasks and workflows with logs, task durations, slack alerts and emails.

Overall, Apache Airflow is designed to make workflow management easier through code. It is a popular platform used by companies for data pipelines and ETL workflows.

Apache Airflow Features

Features

  1. Directed Acyclic Graphs (DAGs) - modeling workflows as code
  2. Dynamic task scheduling
  3. Extensible plugins
  4. Integration with databases, S3, and other environments
  5. Monitoring, alerting, and logging
  6. Scalable - handles data pipelines across organizations
  7. Web server & UI to visualize pipelines

Pricing

  • Open Source

Pros

Open source and free

Active community support

Modular and customizable

Robust scheduling capabilities

Integration with many services and databases

Scales to large workflows

Cons

Steep learning curve

Can be complex to set up and manage

Upgrades can break DAGs

No native support for real-time streaming

UI and API need improvement


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