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Apache Airflow

by pcloudhosting

Free Trial
(1 ratings)

Version 3.3.2 + Free Support on Ubuntu 24.04

Apache Airflow 3.3.2 is an open-source workflow orchestration platform designed to develop, schedule, monitor, and manage complex data pipelines and automated workflows. It provides a web-based interface that allows developers and administrators to monitor DAGs, task executions, logs, schedules, and workflow performance efficiently.

The solution supports common workflow automation and data engineering operations including DAG scheduling, task dependency management, workflow monitoring, logging, retries, and automated execution. It is ideal for data pipelines, ETL workflows, DevOps automation, machine learning pipelines, and cloud-based workflow orchestration use cases.

Version: Apache Airflow 3.3.2

Features of Apache Airflow:

  • Web-based interface to monitor and manage workflows and DAGs.
  • Schedule and automate complex data pipelines and tasks.
  • Task dependency management and workflow orchestration.
  • Detailed task logs, execution history, and monitoring.
  • Supports retries, scheduling, triggers, and task automation.
  • Extensible architecture with support for multiple executors, providers, and integrations.

Usage instructions for Apache Airflow 3.3.2
$ sudo su
$ systemctl status airflow-api
$ systemctl status airflow-scheduler
$ systemctl status airflow-dag-processor
$ systemctl status airflow-triggerer
$ /opt/airflow/venv/bin/airflow version

Credentials Saved in: cat /root/airflow-credentials.txt

Access the Apache Airflow dashboard:

Open your browser and navigate to:

http://your-server-ip:8080

Default Username: admin

To view the generated password:

cat /root/airflow-credentials.txt

Airflow Port: 8080

Disclaimer: Apache Airflow 3.3.2 is provided “as is” under applicable open-source licenses. Users are responsible for proper configuration, authentication, network security, workflow validation, and secure handling of application data. This solution is suitable for workflow orchestration, data pipeline automation, DevOps operations, and development or production environments when configured according to appropriate security and database best practices.

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