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Language Learning Podcast Episodes Extractor DE

Scrape episodes and audio URLs from German language learning podcasts. Get show metadata and export data in CSV or JSON format.

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Apple Podcasts Scraper — Episodes, Audio URLs & RSS Data
Apple Podcasts Scraper — Episodes, Audio URLs & RSS Datalogiover/apple-podcasts-episode-scraper
Episode
Podcast
Author
Released
+4 fields
Text
Number
Boolean
List
Object

Input

Search Terms:english language learning deutsch
Podcast IDs (iTunes Collection IDs)
RSS Feed URLs
iTunes Storefront Country:de
Max Podcasts Per Search Term:25
Max Episodes Per Podcast:80
Use RSS Feed For Full Archive:true
Minimum Release Date (ISO)
Maximum Release Date (ISO)
Explicit Content Filter:all
Min Duration (seconds):0
Include Show Metadata:true

Output fields

Episode
Podcast
Author
Released
Duration (sec)
Primary genre
Audio URL
Apple episode URL

How it works

Sign up on Apify or log in

The Apple Podcasts Scraper — Episodes, Audio URLs & RSS Data Actor is ready to run with the inputs already set for "Language Learning Podcast Episodes Extractor DE". Sign up or log in and you'll land in Apify Console with a copy of this exact task.

Start the "Language Learning Podcast Episodes Extractor DE" task

The Apple Podcasts Scraper — Episodes, Audio URLs & RSS Data Actor will start running based on the input automatically. You can also change all the input fields, like search terms, podcast ids (itunes collection ids), and more, to match your specific use case.

Receive the "Language Learning Podcast Episodes Extractor DE" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as episode, podcast, author, and more.

Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "Language Learning Podcast Episodes Extractor DE" task via API.

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Connect "Language Learning Podcast Episodes Extractor DE" with 100+ integrations, or run it via API or MCP.

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