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.
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.
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.
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 ;)
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:
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.