Programmatically access metadata for millions of digitised books including titles, authors, publishers, descriptions, and full-text previews from Google Books. Useful for building publication catalogues, literature analysis pipelines, and enriching book recommendation systems with structured bibliographic data in Python.
Engineers query the Google Books API with `requests`, using the `q` parameter for full-text search and `volumeId` for individual book details. Results include ISBNs, descriptions, categories, and preview links, which are stored in PostgreSQL for catalog applications.
Google Books data is valuable for building AI-powered book recommendation and summarization tools. Book descriptions serve as training data for genre classifiers, while RAG pipelines can retrieve book metadata to answer questions like 'What Python books cover data engineering at an intermediate level?'
# pip install requests
import requests
resp = requests.get(
"https://www.googleapis.com/books/v1/volumes",
params={"q": "python data engineering", "maxResults": 5}
)
for item in resp.json()["items"]:
print(item["volumeInfo"]["title"])Official dataset source
More datasets used by Python data engineers.
Provides structured data about Breaking Bad characters, episodes, quotes, and deaths. A clean, well-documented REST API commonly used to practise JSON ingestion, relational data modelling, and building small ETL pipelines in Python before working with larger production data sources.
A lightweight REST API that returns random facts and trivia about cats. Useful for learning API integration, testing HTTP client libraries in Python, and building practice ETL pipelines before connecting to more complex data sources.
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