Apache Airflow is a platform created by the Apache Software Foundation to programmatically author, schedule, and monitor workflows. It proves to be especially effective when it comes to cloud projects involving data processing and machine learning. Polidea proudly contributes to the development of this platform.
Customize and implement the Airflow tool according to your specific business needs.
Train your data engineers on how to move your infrastructure to Airflow and guide them through the tool.
Help you become a valued member of the open-source community by mentoring and teaching you about Airflow implementation.
Apache Airflow committers
Project Management Committee member
of all the contributions in the project since it’s kickoff!
As part of the open-source community, Polidea team developed and implemented an extensive set of operators for the Airflow system to work with different cloud service providers. Our Airflow committers contributed to the Open Source Airflow Project and provided 70+ operators for the Airflow DAGs, meeting the highest standards of open-source projects. As a result, the process of building multidimensional workflows of data turned out to be faster than ever before.
Many Apache Airflow users face performance issues. This is manifested by a delay in scheduling tasks, as well as excessive use of resources. Together with Databand, we’ve made major progress in optimizing Airflow scheduler performance. As a result, tests are showing 10x faster performance with over 2000 fewer queries by count.
Seeing the need for improving the Airflow development process and making it more efficient, our committer created a tool called Breeze. It’s an easy-to-use development environment, available for local use, and in Airflow CI tests. Thanks to our solution and involvement in the Apache community, developing Airflow is now faster and simpler for everyone.
How does Apache Airflow work?
Apache Airflow is one of the most highly-recommended schedulers that executes tasks depending on each other in a precise way, set up as a code. As part of the Apache Open Source software projects, it is developed by the whole community of skilled software engineers, which makes it more bullet-proof than any other.
Both Airflow itself and all the workflows are written in Python. This has a lot of benefits, mainly that you can easily apply good software development practices to the process of creating your workflows. These include code versioning, unit testing, avoiding duplication by extracting common elements etc. Moreover, it provides an out-of-the-box browser-based UI where you can view logs, track execution of workflows, and order reruns of failed tasks, among other things.
Do I need a special team to do a project in Airflow?
You can, of course, try to hire developers with a specific set of cloud skills internally, however, it might take time and money. Remember, cloud OSS tools do not come with paid support. The better option would be to hire a team of experts externally, preferably engineers who are involved with the Airflow project itself, like Apache committers and contributors. Lucky for you, some of them are at Polidea ;)
When should I consider Apache Airflow services?
Think of Airflow as an orchestration tool to coordinate work done by other services. It’s not a data streaming solution—even though tasks can exchange some metadata, they do not move data among themselves. Here are some examples of use cases suitable for Airflow:
Will Apache Airflow boost my team’s productivity?
Short answer—yes! It speeds up the work for your data scientists. Additionally, you can also speed up and simplify Airflow and workflow development and testing by using Breeze—a tool co-designed by Polidea.