![]() So I’m using that, well a sub-set of that. The NYC Taxi and Limousine Commission (TLC) exposes each month’s taxi data as CSVs, for free. Understanding the sample workflowĮven though the dataset itself isn’t important for this Airflow workflow, I just want to touch upon this dataset as this is one of the most frequently used dataset in the world of data science. Don’t worry, it’s pretty simple and shouldn’t take more than a couple of minutes if you already have MySQL or Postgres already installed. This is because Derby gives some issues with scheduling DAGs and running them, even locally. Once you have Airflow up and running, make sure you switch from Derby to either MySQL or PostgreSQL as the backend database. If this is you, you’d want to checkout the installation instructions, because there are a bunch of ways to install Airflow. Now that we understand at a very high level what Apache Airflow is, let’s look a simple example workflow to see how we can utilize Airflow.īefore we get started, if you are anything like me, you would want to have Airflow setup locally so that you can just copy-paste the code and see if it works. You can see the full list of current providers here. And as you can expect with any other popular open source platform, there are already a bunch of providers to provide a rich ecosystem. This is different from plugins in the way that a providers package can include new operators, sensors, hooks, and transfer operators to extend Airflow. If you have experience with Python, you’d understand this looking at the documentation.Īnother way to extend the functionality of Airflow is to use providers packages. This should be fairly simple as writing a plugin is mostly as simple as writing a Python package. Obviously you can write your own plugins as well. Similar to operators, there’s support for plugins as well, where you can integrate third party plugins to bring in more functionality to Airflow. Similarly, if you want to run a execute a bash command or run a bash script file, you can use the BashOperator. For example, in a DAG if you want to have an operation just to denote a virtual entity, you can use a DummyOperator. And of course, there are various operators. Airflow uses the same concept to chain various operations together in a workflow.Įach operation in Airflow is defined using an operator. Spark jobs are internally converted to DAGs as well, which you can see visually from the web UI whenever a Spark job is running. This shouldn’t be new to you if you’ve already worked with tools such as Apache Spark. Wait, what’s a DAG, you ask? DAG stands for Directed Acyclic Graph. Oh, by the way, Airflow DAGs are written in Python. As I already mentioned, you can have versioning on Airflow code and easily update or rollback the code at will. It can be programmatically authored, which means you can write some code to not just define the workflow, but also to schedule and update it. Like most Apache projects, this is community driven and open source. That should give a high-level understanding of what Airflow is. The official definition of Airflow on it’s Apache homepage is:Īirflow is a platform created by the community to programmatically author, schedule and monitor workflows.
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