An Operator defines a single activity or task that is represented as a node in the DAG graph in Airflow.ETL solutions such as Informatica, IBM DataStage and others have steep learning curves and even steeper price tags.
![]() Lyft, Robinhood, Roche, Blue Apron among others are big contributors. How did Airflow get created At AirBnB, like other Internet companies of its ilk, there are heaps of data that have to be sifted through. Data Scientists and the company as a whole demanded better data engineering to make better use of the gold mine of data they had. The early version of Airflow, open-sourced, came out of this need. This very early on allowed data engineers to build out, orchestrate and monitor data pipelines. You will struggle to find a top organization not using it in some form or another. What is Airflow in Simple Terms Apache Airflow is designed to build, schedule and monitor data pipeline workflows. The beauty of it is that it is totally free, open-source and is often only limited by your Python skills. There is a large community contributing ideas, operators and features. The fact that its Python based means you can connect to any Python APIs or databases to pull data out, transform and load to the designated target database. Its scheduling capabilities takes data engineering well beyond CRON to something far more usable. The key concept in Airflow are the workflows built as Directed Acyclic Graphs (DAGs). Apache Airflow Code Driven AndETL instead of being drag-and-drop and inflexible, like Informatica, is now Python and code driven and very flexible. Because code is used, it is far more customizable and extensible. DAGs Here is an example of a DAG ( Directed Acyclic Graph ) in Apache Airflow. Note how the tasks that need to be run are organized according to the dependencies, and the order in which they get executed. DAG can be considered the containing structure for all of the tasks you need to execute. DAGs also link up tasks and demonstrate relationships and how everything connects and is dependent. DAGs in Airflow can be shown in a well-designed user interface. They give view to each step in the workflow, but the actual work is done by the Operators in Airflow.
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