We extract course metadata, dynamic pricing signals, instructor profiles, and student reviews from Udemy. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Course Listings objects from udemy.com. All fields typed and schema-versioned.
"course_id": "1565838", "title": "Complete Python Bootcamp From Zero to Hero in Python", "price": 3499.0, "discount_price": 449.0, "rating": 4.6, "enrolment_count": 1823941, "bestseller_badge": true, "duration_hours": 22.5
| # | course_id | title | headline | url | price | discount_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructor Profiles objects from udemy.com. All fields typed and schema-versioned.
"instructor_id": "2467758", "name": "Dr. Angela Yu", "job_title": "Developer and Lead Instructor", "average_rating": 4.7, "total_reviews": 854120, "total_students": 2512901, "course_count": 9
| # | instructor_id | name | job_title | profile_url | average_rating | total_reviews |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Student Reviews objects from udemy.com. All fields typed and schema-versioned.
"review_id": "89123412", "course_id": "1565838", "user_name": "Rahul M.", "rating": 5.0, "content": "Excellent pacing for beginners.", "created_at": "2023-11-14T10:23:00Z", "helpful_votes": 12
| # | review_id | course_id | user_name | rating | content | created_at |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Curriculum Data objects from udemy.com. All fields typed and schema-versioned.
"course_id": "1565838", "section_index": 3, "section_title": "Python Object Oriented Programming", "lecture_index": 14, "lecture_title": "Classes and Objects", "content_type": "video", "duration_minutes": 14.5, "is_free_preview": false
| # | course_id | section_index | section_title | lecture_index | lecture_title | content_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from udemy.com. All fields typed and schema-versioned.
"keyword": "machine learning", "position": 2, "course_id": "903744", "title": "Machine Learning A-Z", "badges": "['Bestseller']", "discount_price": 449.0, "scraped_at": "2023-11-15T08:12:44Z"
| # | keyword | position | course_id | title | price | discount_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Udemy scraper navigates dynamic pricing algorithms, localized currency displays, and nested curriculum structures to deliver clean, relational datasets.
Extract titles, descriptions, requirements, target audiences, and learning objectives for any course category.
Capture base prices and flash sale discounts across different geographic regions using localized IP addresses.
Track instructor performance metrics including total students, average ratings, and cross-course enrolment patterns.
Paginate through thousands of student reviews to extract textual feedback, star ratings, and helpfulness scores.
Parse nested JSON structures to map sections, lectures, video durations, and preview availability.
Identify Bestseller and Highest Rated badges to monitor category leaders and trending courses.
Configure pipelines to extract data as it appears in India, the US, the UK, or any other target market.
Track organic search positions for specific skills and tools to understand platform SEO.
Maintain hash indexes to only export new courses, updated prices, or fresh reviews on subsequent runs.
Brief in. Clean data out.
Provide category URLs, instructor IDs, or keyword lists. We design the schema to match your analytical needs.
We configure Playwright crawlers, manage regional proxies for pricing, and handle Cloudflare protections.
Schema validation, null-rate checks, and price anomaly detection before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on schedule.
Udemy relies heavily on client-side rendering and aggressive caching. Here is how we build resilient pipelines to bypass these hurdles.
Udemy prices change based on user location, cookie history, and active flash sales. We use clean residential proxies and isolate browser contexts to capture the true baseline or discounted price for a specific region.
Udemy protects its internal APIs with Cloudflare. Our infrastructure utilizes TLS fingerprinting and automated solver integrations to maintain API access without triggering blocks.
Course curriculums are deeply nested JSON objects. We parse and flatten these structures into relational tables, linking lectures and sections back to the parent course ID.
Extracting thousands of reviews requires precise API pagination and session management to avoid rate limits and capture the entire historical corpus.
With hundreds of thousands of active courses, full catalogue refreshes require distributed crawling. We use Apache Airflow to orchestrate parallel tasks across Kubernetes clusters.
Competing platforms monitor Udemy course catalogues, pricing strategies, and instructor acquisitions to identify content gaps.
HR and L&D teams extract course metadata to evaluate and integrate third-party content into their internal learning management systems.
Content creators analyse category demand, average enrolments, and student feedback to plan their next course syllabus.
Machine learning teams use course descriptions and curriculum structures to train educational recommendation engines and skill taxonomy models.
Analysts track discount frequencies and regional price variations to understand price elasticity in the online education market.
Product teams mine student reviews to identify common complaints about video quality, outdated software, or teaching styles.
"Udemy represents the largest structured dataset of professional skills and learning pathways available on the public web."
Accessing this data requires navigating dynamic pricing algorithms, Cloudflare protections, and deeply nested JSON APIs. DataFlirt manages the extraction infrastructure so your data science teams can focus on mapping skill taxonomies and analyzing market demand.
Everything supported by our udemy.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles category traversal and queue management. Playwright executes JavaScript to trigger localized pricing and bypass client-side security checks.
We route requests through specific regional ISP proxies to capture accurate local pricing, bypassing IP-based currency redirection.
Pipelines run on Kubernetes clusters managed by Apache Airflow, enabling parallel extraction of massive course categories without triggering rate limits.
Data delivered to where your team already works — no new tooling required.
About udemy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available metadata, such as course titles, prices, and public reviews, is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract paid video content, proprietary course materials, or user personal data. Clients should consult legal counsel regarding their specific use cases.
Udemy prices vary by region and active promotions. We use geographically targeted residential proxies and isolated browser sessions to capture the exact price displayed to a user in a specific country.
Yes. We extract the public syllabus, including section titles, lecture names, video durations, and preview availability. We do not extract the actual video files or gated content.
We utilize advanced TLS fingerprinting, realistic browser headers, and automated solver integrations to navigate Cloudflare turnstiles without interrupting the extraction pipeline.
We can configure pipelines to run daily or weekly depending on your requirements. Daily runs are typical for monitoring flash sales and dynamic pricing changes.
Yes. We can capture instructor total student counts, average ratings, and review volumes on a scheduled basis, delivering a time-series dataset for trend analysis.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete category extraction or continuous monitoring of instructor metrics, we scope, build, and operate the pipeline. Tell us what you need.
