Python ETL pipeline with News API

Dev.to published a step-by-step guide to building a Python ETL pipeline that pulls articles from the News API into a PostgreSQL table [DevTo]. The guide starts by creating a virtual environment and installing requests, pandas, sqlalchemy, and python-dotenv. It then shows how to store a News API key in a .env file and fetch articles about "apple" for a single day using a GET request [DevTo]. The response is parsed to JSON, and the HTTP status code is printed to confirm a 200 success. Next, the guide drops the source and urlToImage columns from the article list, builds a pandas DataFrame, and prints the head of the frame. The transformation step is wrapped in a transform_data function that catches KeyError if the columns are missing. Finally, the tutorial wires a PostgreSQL connection string together with sqlalchemy.create_engine and calls DataFrame.to_sql to append the cleaned rows to an articles table [DevTo]. The full code lives in a single script, but the author notes that Jupyter or Colab can be used for rapid iteration. By providing a concrete ETL example, the guide lowers the barrier to production data pipelines. The tutorial also clarifies the ETL vs. ELT debate with a real-world use case, demonstrating the importance of transforming data before loading to keep the target database lean [DevTo]. Additionally, the code demonstrates best-practice security hygiene by storing the API key and database credentials in a .env file, avoiding hard-coding secrets [DevTo].