ETL Pipeline Example: Airflow Extraction Task
Understand how to orchestrate ETL pipelines using Apache Airflow by integrating a Python extraction function as a task within a DAG. Learn to use Airflow operators, especially PythonOperator, to automate and schedule data extraction from production databases, verifying task success and preparing for subsequent ETL steps.
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After completing the previous task, we now have a function called extract for extracting the latest batch of data from the production database in a file called helper.py. We’ll add this function as a task in an airflow DAG to complete the extract step.
This is how we build our pipelines using airflow, by adding more and more tasks to a particular DAG.
Airflow operators
Before showing how to do that, let’s discuss airflow operators. In Airflow, operators are the building blocks that define tasks in a pipeline. Each operator is different and can be used to perform a particular type of task, such as executing a SQL query, transferring files, running a Python script, etc. Some common operators are: ...