Apache Airflow is an excellent choice for orchestrating ETL (Extract, Transform, Load) workflows, particularly when dealing with data transfers between S3 buckets and Snowflake. Below is an outline of how you can set up an Airflow DAG (Directed Acyclic Graph) to achieve the following:
Download Files from Source S3: Using Airflow’s PythonOperator and S3Hook, files are listed and downloaded from the source S3 bucket to a local directory on the Airflow worker.
Upload Files to Destination S3: The downloaded files are then uploaded to a specified destination S3 bucket using the same S3Hook, ensuring the files are organized under a defined prefix.
Load Files to Snowflake: The S3ToSnowflakeOperator is employed to copy the files from the destination S3 bucket to a Snowflake stage. This operator handles the data transfer and formats it for ingestion into a specified Snowflake table.
