Apache Airflow Managed Service

Apache Airflow is open-source software that lets you programmatically author, schedule and monitor data pipelines. Airflow is written in Python, and workflows are created via Python scripts. Thanks to plug-and-play operators, it is possible to execute tasks on Microsoft Azure, Google Cloud Platform, or Amazon Web Services. Apache Airflow provides an API interface and a WebUI interface for diagram visualization and monitoring. A log, task history and jinja templates are available, which significantly
expands the possibilities of the code.

Apache Airflow Main Principles:

Apache Airflow Services We Perform

With our extensive practical experience, we can provide comprehensive services for Apache Airflow








Airflow implementation


Testing and debugging


Your benefits with Airflow:

How can your organization benefit from a Cloud Solution?

Item 1
Item 2
Item 3
Item 4
Item 5
Item 6
Item 7

Our Clients

Here are some of our clients that trust us with their online solutions 

Our team of 40+ experts has successfully delivered projects for global clients

Areas of Airflow implementation

Apache Airflow FAQ

Apache Airflow is an open-source workflow management system that helps users programmatically author, schedule, and monitor data pipelines. It provides a web-based interface for creating and managing workflows, as well as a REST API for programmatic access.

Some benefits of using Apache Airflow include:
  • Flexibility: Airflow supports a wide range of operators and executors, making it easy to integrate with various data sources and processing tools.
  • Scalability: Airflow can be deployed on a variety of platforms, including Kubernetes, Docker, and AWS EMR, and can handle large-scale workloads with ease.
  • Collaboration: Airflow provides a centralized platform for managing workflows, making it easy for teams to collaborate and share resources.
  • Monitoring: Airflow provides detailed monitoring and logging capabilities, making it easy to track the progress of workflows and identify issues.
Some key features of Apache Airflow include:
  • Directed Acyclic Graph (DAG): Airflow uses a DAG to represent workflows, making it easy to visualize and manage dependencies between tasks.
  • Operators: Airflow provides a library of operators for common data processing tasks, such as loading data from a database or running a Python script.
  • Executors: Airflow supports a variety of executors, including local, Kubernetes, and YARN, making it easy to run tasks on different platforms.
  • Scheduling: Airflow provides a flexible scheduling system that supports cron-style expressions, as well as dynamic scheduling based on data availability.
  • Monitoring: Airflow provides detailed monitoring and logging capabilities, including email notifications, Slack integration, and a web-based UI for tracking workflow progress.

Apache Airflow is licensed under the Apache License, Version 2.0.