Apache Airflow- Complete Guide

Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It is often used for data pipelines, but can also be used for other types of tasks. Airflow was developed by Airbnb and is now maintained by the Apache Software Foundation. It is written in Python and is built on top of Apache Mesos and Apache Cassandra.

Compatibility

1. Amazon Web Services (AWS): Airflow can be easily deployed on AWS using EC2 instances, ECS, or EMR.
2. Google Cloud Platform (GCP): Airflow can be run on GCP using Google Compute Engine (GCE) instances or Google Kubernetes Engine (GKE) clusters.
3. Microsoft Azure: Airflow can be deployed on Azure using Virtual Machines or Kubernetes Service.
4. Alibaba Cloud: Airflow can be run on Alibaba Cloud using Elastic Compute Service (ECS) instances or Kubernetes Service (ACK).
5. OpenStack: Airflow can run on OpenStack clouds using Nova instances.

Additionally, Airflow also supports running on on-premises infrastructure using a cluster manager like Apache Mesos, Kubernetes, or Docker.

Sample Architectural Diagram

• The Airflow UI provides a web-based interface for managing workflows and monitoring the status of tasks.
• The Airflow API is the backend service that serves the UI and is responsible for scheduling and executing tasks.
• The Airflow Worker is the component that actually runs the tasks. It can run on a single machine or on a cluster of machines.
• The database (usually a metadata database) is used to store information about the workflows and tasks, such as their status and execution history.
• External services are the services that the tasks interact with such as data warehouses, message queues, and APIs.

Advantages

1. Flexibility: Airflow allows users to author workflows in Python, which makes it easy to integrate with existing systems and services. Additionally, Airflow’s modular architecture allows users to add custom functionality and extend the platform to fit their specific needs.
2. Scalability: Airflow can handle a large number of tasks and can scale to fit the needs of large organizations. It can also be run on a cluster, which allows for even more scalability.
3. Monitoring and Alerting: Airflow has built-in support for monitoring and alerting, which allows users to keep track of the status of their workflows and quickly identify and troubleshoot issues.
4. Extensible: Airflow has a large and active community, which means that there are many third-party plugins and integrations available.
5. Web UI: Airflow has a web-based user interface that makes it easy to visualize, monitor, and manage workflows.
6. Multi-cloud Support: Airflow can run on multiple cloud platforms like AWS, GCP, and Azure.
7. Easy to use: Airflow is easy to use and understand, especially for those with a background in Python.

All in all, Airflow is a powerful, flexible, and well-supported tool that can help organizations manage and automate their workflows.

Competitors of Apache Airflow

• Luigi: An open-source Python package developed by Spotify for building complex pipelines.
• Jenkins: A popular open-source automation server that is often used for CI/CD and building pipelines.
• AWS Glue: A fully managed extract, transform, and load (ETL) service that is part of the Amazon Web Services (AWS) ecosystem.
• Azure Data Factory: A cloud-based data integration service that is part of the Microsoft Azure ecosystem.
• Google Cloud Composer: A fully managed workflow orchestration service that is part of the Google Cloud ecosystem.
• Prefect: A open-source workflow management system that supports data engineers, data scientists, and machine learning engineers.

It’s worth noting that some of these tools are more focused on specific use cases, such as Jenkins for CI/CD, Glue for ETL, and Composer for GCP.