The Spark job has to wait for the three “read” tasks and populate the data into S3 and HDFS.
Once that is completed, we initiate a Spark job to join the data on a key and write the output of the transformation to Redshift.ĭefining a DAG enables the scheduler to know which tasks can be run immediately, and which have to wait for other tasks to complete. We will work on this example DAG that reads data from 3 sources independently.
DAGs are a high-level outline that define the dependent and exclusive tasks that can be ordered and scheduled. In Airflow, Directed Acyclic Graphs (DAGs) are used to create the workflows. The DAG that we are building using Airflow Let’s look at few concepts that you’ll need to write our first workflow. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. This removes the need to use restrictive JSON or XML configuration files. Python Based: Every part of the configuration is written in Python, including configuration of schedules and the scripts to run them.You can also work with the command line, but the web interface is more intuitive. Web Interface: Airflow ships with a Flask app that tracks all the defined workflows, and let’s you easily change, start, or stop them.It is currently maintained and managed as an incubating project at Apache. This was a major reason why it eventually became an open source project. Open source: After starting as an internal project at Airbnb, Airflow had a natural need in the community.There are numerous resources for understanding what Airflow does, but it’s much easier to understand by directly working through an example.
It gets complicated if you’re waiting on some input data from a third-party, and several teams are depending on your tasks to start their jobs.Īirflow is a workflow scheduler to help with scheduling complex workflows and provide an easy way to maintain them. This is really good for simple workflows, but things get messier when you start to maintain the workflow in large organizations with dependencies. To automate this pipeline and run it weekly, you could use a time-based scheduler like Cron by defining the workflows in Crontab. For example, a pipeline could consist of tasks like reading archived logs from S3, creating a Spark job to extract relevant features, indexing the features using Solr and updating the existing index to allow search. Why Airflow?ĭata pipelines are built by defining a set of “tasks” to extract, analyze, transform, load and store the data.
#Airflow etl code
In this blog, I cover the main concepts behind pipeline automation with Airflow and go through the code (and a few gotchas) to create your first workflow with ease.
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