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Big Data Test Infrastructure (BDTI)

Apache Airflow

Apache Airflow allows to programmatically author and schedule workflows and monitor them via a built-in Airflow user interface. Airflow is written in Python and is designed under the principle of "configuration as code".

Airflow exists of multiple building blocks such as:

  • A DAG (a Directed Acyclic Graph) which represents a workflow, and contains individual pieces of work called Tasks, arranged with dependencies and data flows taken into account
  • A scheduler, which handles both triggering scheduled workflows, and submitting Tasks to the executor to run
  • An executor, which handles running tasks
  • A webserver, which presents a handy user interface to inspect, trigger and debug the behaviour of DAGs and tasks
  • A folder of DAG files, read by the scheduler and executor
  • A metadata database, used by the scheduler, executor and webserver to store state
  • A built-in Airflow user interface (multiple Airflow UI visualizations exist)

Please find the different Airflow UI visualizations here.



Find below some of the main features of Apache Airflow:

  • A useful UI: making it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed
  • Robust Integrations: Apache Airflow allows many plug-and-play operators that are ready to execute your tasks on third party-services such as Apache Spark, MongoDB, and many more
  • Easy to use: easily deploy python-based workflows, built ML models, transfer data, manage infrastructure, and so on

Use Cases

Find below some examples of possible use cases:

  • Building an orchestration engine (scheduling and executing various types of workflows)
  • Orchestrating SQL transformations in databases
  • Orchestrating functions against clusters for ML models
  • Increasing the visibility of batch processes and decoupling batch jobs


Find below some interesting links providing more information on Apache Airflow:

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