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Access YouTube video metadata, channel statistics, playlist data, comments, captions, and trending content. Used in data pipelines for social media analytics, content trend monitoring, comment sentiment analysis, and building video performance dashboards using the Google API Python client.
The `google-api-python-client` library provides the official YouTube Data API v3 client. Engineers paginate through channel videos, collect comment threads, and aggregate statistics into BigQuery for creator analytics and trend detection pipelines.
YouTube transcript data is excellent for training and fine-tuning LLMs in specific domains. Video descriptions and comment threads power RAG systems for content discovery, while sentiment analysis on comments trains AI models for engagement prediction and moderation.
# pip install google-api-python-client
from googleapiclient.discovery import build
youtube = build("youtube", "v3", developerKey="YOUR_API_KEY")
response = youtube.search().list(
q="python data engineering tutorial",
part="snippet", maxResults=5
).execute()
for item in response["items"]:
print(item["snippet"]["title"])Official dataset source
More datasets used by Python data engineers.
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.
Access music metadata, audio features (tempo, energy, danceability), playlist data, artist catalogues, and listening history from the Spotify platform. Used in data engineering for building music recommendation systems, audio feature datasets, and trend analysis pipelines with the spotipy Python library.
Access repositories, commits, pull requests, issues, users, and organisation data from GitHub. Ideal for building developer analytics pipelines, tracking open-source project activity, and ingesting code metadata into data warehouses using Python and the PyGitHub library.