10 Running tasks in containers
This chapter covers
- Identifying some challenges involved in managing Airflow deployments
- Examining how containerized approaches can help simplify Airflow deployments
- Running containerized tasks in Airflow on Docker
- Establishing a high-level overview of workflows in developing containerized DAGs
In previous chapters, we implemented several DAGs using different Airflow operators, each specialized to perform a specific type of task. In this chapter, we touch on some of the drawbacks of using many different operators, especially with an eye on creating Airflow DAGs that are easy to build, deploy, and maintain. In light of these issues, we look at how we can use Airflow to run tasks in containers using Docker and Kubernetes and some of the benefits this containerized approach can bring.
10.1 Challenges of many different operators
Operators are arguably one of the strong features of Airflow, as they provide great flexibility to coordinate jobs across many different types of systems. However, creating and managing DAGs with many different operators can be quite challenging due to the complexity involved.