Apache Airflow Managed Services
Optimize Your Data Pipelines with Apache Airflow Managed Services
Streamline Your Data Workflows
Effortlessly manage your data workflows with our fully managed Apache Airflow service. We simplify pipeline automation and scaling, allowing your team to focus on insights and strategy rather than maintenance. Leave the heavy lifting to us—your data deserves better.
What is Apache Airflow?
Apache Airflow is an open-source platform for creating, scheduling, andmonitoring data workflows in Python. With ready-to-use operators, it supportstasks across cloud platforms like Azure, Google Cloud, and AWS. Airflow’s APIand WebUI simplify visualization and monitoring, while features like logs, taskhistory, and Jinja templates enhance workflow flexibility and control.
Apache Airflow: Core Principles
Scalable
Modular architecture allows limitless scaling.
Dynamic
Python-based, enabling flexible pipeline generation.
Open Source
Community-driven with no barriers to entry.
Extensible
Easily customizable to fit unique environments.
Elegant
Streamlined, clear, and user-friendly workflows.
Apache Airflow: Core Principles
Deployment & Monitoring
Set up and oversee Airflow instances.
Migration
Transfer both instances and workflows
Upgrades
Keep Airflow current with the latest versions.
Issue Resolution
Troubleshoot and fix Airflow components and bugs.
DAG Development
Craft custom workflows with diverse operators.
Plugin Creation
Develop plugins tailored to your needs.
.svg)
Design
First step is a decision about the platform (whether on-premises or cloud-based). We consider factors like hardware scaling and fault tolerance. Software selection involves choosing the necessary components and adopting a tailored workflow build approach to ensure seamless operations. Security measures include implementing Single Sign-On (SSO) authentication, utilizing key vaults for credential and sensitive data storage, and designing multi-level access controls for specific user groups.
.svg)
Installation
At this stage, all focus lies on setting up all prerequisites on the chosen platform to ensure a smooth deployment process. This involves installing Airflow in the designated environment, be it bare metal, virtual machines, Docker containers, or orchestrated using Kubernetes, enabling streamlined workflow management and efficient data processing.
.svg)
Implementation
During the implementation stage, key tasks involve developing Directed Acyclic Graphs (DAGs) in Python, encompassing static and dynamic workflows. This process includes creating custom operators when standard options are insufficient for specific tasks. Automated monitoring and alert systems ensure streamlined workflow operation. Custom user interfaces, integrated with Airflow using JavaScript, are built to facilitate task triggering based on user input. Continuous monitoring of DAG execution, log access, and other functionalities ensure smooth workflow progress.
.svg)
Testing and Debugging
We conduct thorough testing to identify and rectify any potential issues and ensure the solution functions as intended. In case of errors, comprehensive debugging is performed, including source code analysis for both the solution and the Airflow framework itself.
What our clients say
Anonymous
CEO, Sports Analytics Company
Maciej Mościcki
CEO, Macmos Stream
Adam Murray
Head of Product Development, Sportside
Anonymous
CEO, Sports Analytics Company
Maciej Mościcki
CEO, Macmos Stream
Adam Murray
Head of Product Development, Sportside
Selected Clients
Unlock the full potential of your data with our Apache Airflow Managed Services
Why Choose Our Apache Airflow Managed Services?
Seamless Integration & Reliable Performance
Integrate seamlessly with your existing tech stack. Our Apache Airflow service is designed to enhance your current workflows with minimal disruption, ensuring robust, scalable, and efficient data management. We handle the infrastructure, updates, and troubleshooting, so you don’t have to.
Tailored Monitoring & Proactive Support
With our managed service, you get end-to-end monitoring and round-the-clock support. Our dedicated experts proactively manage your workflows, quickly resolving any issues and keeping your data pipelines running smoothly.
Automatic Scaling & Optimized Resource Usage
As your business grows, so does your data. Our Apache Airflow service automatically scales to meet increased data loads, allowing you to optimize resources and cut costs. Focus on what matters while we keep your workflows efficient and reliable.
Comprehensive Security & Compliance
Data security is our priority. Our managed service includes built-in compliance and security protocols, protecting your sensitive information and meeting industry standards. Trust us to keep your data safe every step of the way.
Drop us a line and check how Data Engineering, Machine Learning, and AI experts can boost your business.
Talk to expert – It’s free
Discover our insights
Technology stack
Let’s talk and work together
We’ll get back to you within 4 hours on working days (Mon – Fri, 9am – 5pm CET).
Service Delivery Partner
Apache Airflow is an open-source workflow management platform started in October 2014 at Airbnb. Airflow lets you programmatically author, schedule, and monitor data workflows via the built-in user interface. Airflow is a data transformation pipeline ETL (Extract, Transform, Load) workflow orchestration tool.
It helps you programmatically control workflows by setting task dependencies and monitoring tasks within each DAG in a Web UI. Airflow offers detailed logs for each task in very complex workflows.
- Scalable: Airflow is ready for infinite scaling.
- Dynamic: Pipelines defined in Python allow for dynamic pipeline generation.
- Extensible: Operators are easily defined.
- Elegant: Airflow pipelines are lean and coherent.
If you are in need of an open-source workflow automation tool, you should definitely consider adopting Apache Airflow. This Python based technology makes it easy to set up and maintain data workflows.