Loading...
Access news articles, headlines, and metadata from over 80,000 sources worldwide, searchable by keyword, source, language, and date. Used in data engineering for media monitoring pipelines, NLP training data collection, topic trend analysis, and real-time news feed ingestion into data warehouses.
The `newsapi-python` client library simplifies authentication and endpoint access. Engineers fetch top headlines or search articles by keyword, date range, and source, ingesting results into Elasticsearch for full-text search or pandas for time-series sentiment analysis.
NewsAPI feeds real-time news context into AI systems, reducing hallucinations on current events. RAG pipelines retrieve recent articles to ground LLM responses with factual, dated information. You can also fine-tune summarization models on news article–headline pairs collected via this API.
# pip install newsapi-python
from newsapi import NewsApiClient
newsapi = NewsApiClient(api_key="YOUR_API_KEY")
articles = newsapi.get_top_headlines(
q="data engineering", language="en", page_size=5
)
for a in articles["articles"]:
print(a["title"], a["source"]["name"])Official dataset source
More datasets used by Python data engineers.
Access NYT articles, book reviews, bestseller lists, movie reviews, event listings, and archive content from 1851 onwards. Used in data engineering for building news analytics pipelines, constructing NLP training corpora, and ingesting structured editorial metadata into data warehouses using Python.
Retrieve Wikipedia article content, summaries, page views, links, categories, and search results programmatically. Commonly used in NLP pipelines for training data collection, knowledge graph construction, entity resolution, and enriching datasets with encyclopedic context using the wikipedia-api Python library.
Access content and metadata from all Wikimedia projects including Wikipedia, Wiktionary, Wikiquote, and Commons. Used in data pipelines for multilingual text corpus construction, knowledge graph enrichment, page view analytics, and building NLP training datasets from structured encyclopaedic content in Python.