This project automates the daily extraction, processing, and storage of Google Trends data for various programming languages across regions. Using Airflow, it retrieves trend data via the pytrends API, structures it in a DataFrame, and stores it in a PostgreSQL database. The Airflow DAG handles data retrieval, dynamic table creation, and data insertion, ensuring a regularly updated dataset for trend analysis.
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The simple ETL with docker container
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- Python 79.2%
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