Building a four-step data pipeline using Airflow,The pipeline will download podcast episodes.
Install the following locally:
- Python 3.8+
- Airflow 2.3+
use the constraints file to setup the airflow locally in a venv.
Python packages: install the required packages using the requirements file
Ensure that your dags are present in the dags folder of $AIRFLOW_HOME.
If the webserver and scheduler are running you can view and trigger your dags at localhost:8080
podcasts pipeline
This code creates a DAG (Directed Acyclic Graph) in Airflow with a single task to download, parse, and extract the list of episodes from the Marketplace podcast metadata. The DAG is scheduled to run once a day and will not catch up for missed runs. The download_parse_extract task uses the requests library to download the metadata from the specified URL, then uses xmltodict to parse the XML data and extract the list of episodes.
pipeline with database
This code adds a new task to Airflow pipeline using the SqliteOperator to create a table in a SQLite database. The create_table task uses the sqlite_default connection ID to connect to the SQLite database and runs an SQL command to create a table named episodes with the specified fields if it doesn’t already exist.
storing data to db
This code adds a new task to Airflow pipeline using the @task decorator to store the episode metadata into the SQLite database.The load_to_db task uses the SqliteHook to connect to the SQLite database and query it to determine which episodes are already stored. It then loops through the list of episodes and inserts new rows into the episodes table for any episodes that are not already stored.
checking sqlite database
This code creates a new DAG in Airflow named check_sqlite_database with a single task that checks the contents of the SQLite database and logs the results. The check_database function uses the SqliteHook to connect to the SQLite database and query it to retrieve all rows from the episodes table. It then loops through the rows and prints each one.
downloading podcasts episodes
This code adds a new task to Airflow pipeline using the @task decorator to download the actual podcast episodes. The download_episodes task loops through the list of episodes and for each episode, it creates a filename and filepath for the corresponding audio file. If the audio file does not already exist, it uses the requests library to download it from the specified URL and saves it to the specified filepath.
You can customize this project in the following ways.
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Schedule the project to run daily, so you'll have a new episode when it goes live.
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Parallelize tasks and run them in the cloud.
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Add speech recognition, summaries using Airflow.
Contributions are welcome! If you have any ideas, improvements, or bug fixes, please open an issue or submit a pull request.
This project is licensed under the MIT License.