Observed Signal · Jun 7, 2026 · Technical Guide · Source: DEV Community · Impact: 1/5 · Sentiment: Neutral
ETL Pipeline: News API to PostgreSQL with Python
A developer tutorial demonstrating how to build a simple ETL pipeline in Python that extracts technology headlines from the News API, transforms nested JSON using pandas, and loads cleaned rows into a PostgreSQL table. The article includes an example SQL schema for a news_articles table, a modular Python script (extract/transform/load), required dependencies (requests, pandas, psycopg2-binary, sqlalchemy), and troubleshooting notes (using pd.to_datetime() to parse ISO timestamps with trailing 'Z'). The author used a PostgreSQL instance hosted on Aiven and notes next steps: automating the pipeline with Apache Airflow.
Beginner-level technical tutorial on building an ETL pipeline using common open-source tools; practically useful for developers but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Tutorial shows an ETL pipeline that extracts top technology headlines from the News API, transforms them with pandas, and loads them into PostgreSQL.
- The article provides a CREATE TABLE script for news_articles with columns: id SERIAL PRIMARY KEY, source VARCHAR(100), author VARCHAR(150), title TEXT, description TEXT, url TEXT, published_at TIMESTAMP, extracted_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP.
- The Python script is modularized into extract, transform, and load functions and lists dependencies: requests, pandas, psycopg2-binary, and sqlalchemy.
- Date parsing issue (ISO timestamp with trailing 'Z', e.g., 2026-06-07T06:00:00Z) was resolved using pandas' pd.to_datetime() before loading to the database.
- Author used a PostgreSQL instance on Aiven and plans a follow-up article on automating the workflow with Apache Airflow (data orchestration).
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