Loading...
A free, open-source database API of breweries worldwide with details on beer types, locations, addresses, and contact information. Useful for practising REST API ingestion, geocoding datasets, building location-based analytics pipelines, and learning geospatial data loading in Python.
Engineers use `requests` to query Open Brewery DB by city, state, type, or postal code, collecting brewery name, coordinates, phone, and website. The free, no-auth API makes it ideal for teaching geospatial data collection and PostGIS ingestion.
Open Brewery DB powers location-aware AI chatbots — expose it as an MCP tool so LLM agents can answer 'What craft breweries are within 10 miles of Portland, OR?' with real data. The brewery catalog also makes a fun domain-specific RAG knowledge base for hospitality AI demos.
# pip install requests pandas
import requests, pandas as pd
resp = requests.get("https://api.openbrewerydb.org/v1/breweries",
params={"by_city": "Portland", "per_page": 10})
df = pd.DataFrame(resp.json())[["name", "brewery_type", "state"]]
print(df)Official dataset source
More datasets used by Python data engineers.
An open-source music encyclopaedia API providing structured data on artists, albums, recordings, labels, and relationships. Used in data engineering for building music catalogues, constructing artist graph datasets, enriching streaming data with metadata, and practising complex JSON ingestion in Python.
Retrieve real-time and historical air quality measurements including PM2.5, PM10, ozone, NO2, and CO from monitoring stations worldwide. Used in environmental data engineering pipelines for pollution trend analysis, public health analytics, geospatial mapping of air quality, and time-series ingestion in Python.
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.