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:
- Scalable – due to its modular architecture Airflow can be scaled infinitely.
- Dynamic – Airflow pipelines are defined in Python, which allows for dynamic pipeline generation.
- Open Source – there are no barriers or lengthy procedures here. A community of many active users is willing to share their experiences.
- Extensible – easy to define operators and extend libraries to fit the environment.
- Elegant – Airflow pipelines are lean, explicit and user friendly.
Apache Airflow Services We Perform
- Deploying and monitoring Airflow instances
- Migrating Airflow instances
- Migrating workflows
- Upgrading Airflow to newest versions
- Resolving issues with Airflow components
- Spotting and fixing Airflow bugs
- Writing DAGs with all kinds of operators
- Writing custom plugins
With our extensive practical experience, we can provide comprehensive services for Apache Airflow
Design
Platform:
- On premises or in the Cloud
- Hardware scaling
- Fault tolerance
Software:
- Required components
- Choosing an approach to prepare the optimal workflow build
Security:
- Implementation of SSO authentication
- key vault utilization for credentials or sensitive data storage
- Designing multiple levels of access for specific target groups
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Installation
- Installation of all prerequisites on the selected platform
- Airflow installation in the selected environment: bare metal, virtual machines, containerized in Docker, orchestrated via Kubernetes
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Airflow implementation
- Building DAGs using Python: static or dynamic
- Building custom operators not available out of the box
- Setting up automated monitoring or alerting for whole workflows or selected tasks
- Building custom user interfaces integrated with Airflow (using JavaScript) via dedicated operators that dynamically trigger building specific tasks or workflows based on user input
- Monitoring the DAG execution progress, access to logs, and other functions
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Testing and debugging
- Testing implemented solutions
- Error debugging, including source code analysis both for an implemented solution and Airflow itself
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Your benefits with Airflow:
How can your organization benefit from a Cloud Solution?
- Low cost
- Fast and smooth DAG synchronization
- The ability to debug issues deep inside Pods or Nodes
- Effective autoscaling
- Newest Airflow releases
- Easy access to logs
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
- Successfully deployed over 70 instances of Airflow on Azure Kubernetes through CICD pipelines with autoscaling, Istio service mesh enabled & monitoring dashboards in Grafana, including alerts
- L4 support for over 70 instances of Apache Airflow in organization
- Migrated over 70 instances of Airflow from VMs to Kubernetes
- Experienced in migrating workflows from Apache Ozzie to Apache Airflow
- Able to spot and fix Airflow bugs without assistance from the Airflow community if it is required in a short period of time
- Experienced in writing custom plugins when necessary
Apache Airflow FAQ
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1. What is Apache Airflow?
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2. What are the benefits of using Apache Airflow?
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3. What are the key features of Apache Airflow?
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4. How is Apache Airflow licensed?
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.
