We extract course catalogues, instructor profiles, lesson breakdowns, and duration metrics from Masterclass. 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 Metadata objects from masterclass.com. All fields typed and schema-versioned.
"course_id": "mc_1042", "title": "Gordon Ramsay Teaches Cooking I", "instructor_name": "Gordon Ramsay", "category": "Food", "total_lessons": 20, "total_duration_seconds": 13920, "release_date": "2017-02-15"
| # | course_id | title | instructor_name | category | description | total_lessons |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructor Profiles objects from masterclass.com. All fields typed and schema-versioned.
"instructor_id": "inst_084", "name": "Gordon Ramsay", "profession": "Chef", "course_count": 2, "profile_image_url": "https://cdn.masterclass.com/images/gordon.jpg", "notable_achievements": "['7 Michelin Stars', 'OBE']"
| # | instructor_id | name | profession | biography | profile_image_url | course_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lesson Plans objects from masterclass.com. All fields typed and schema-versioned.
"lesson_id": "les_9931", "course_id": "mc_1042", "chapter_number": 3, "title": "Vegetables & Herbs", "duration_seconds": 642, "is_preview_available": false
| # | lesson_id | course_id | chapter_number | title | description | duration_seconds |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Taxonomy objects from masterclass.com. All fields typed and schema-versioned.
"category_id": "cat_04", "name": "Food", "slug": "food", "parent_category": "Lifestyle", "course_count": 18, "featured_course_id": "mc_1042"
| # | category_id | name | slug | parent_category | course_count | popular_instructors |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Plans objects from masterclass.com. All fields typed and schema-versioned.
"plan_id": "plan_individual_us", "region": "US", "currency": "USD", "annual_price": 120.0, "monthly_equivalent": 10.0, "device_limit": 1
| # | plan_id | region | currency | annual_price | monthly_equivalent | features_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Masterclass scraper maps the entire platform taxonomy. We extract full course structures, instructor biographies, lesson metadata, and pricing tiers using automated state parsing and anti-bot circumvention.
Extract titles, descriptions, categories, and total duration metrics for every course in the Masterclass catalogue.
Capture full biography text, professional backgrounds, and related instructor networks.
Map every chapter within a course, including lesson titles, descriptions, and exact video duration in seconds.
Scrape the complete taxonomy structure, mapping parent categories to specific sub-genres and tags.
Track subscription tiers, family plans, and promotional pricing across different geographic regions.
Extract preview video URLs and high-resolution thumbnail assets for every course and instructor.
Masterclass is a heavy React application. We parse Next.js state directly to extract clean JSON data without relying on brittle DOM selectors.
Map related courses and recommended learning paths to understand internal content grouping.
Run pipelines weekly or monthly to catch new class drops and instructor additions automatically.
Brief in. Clean data out.
Provide specific categories, instructor names, or request a full catalogue crawl. We design the schema together.
We configure state parsers, proxy rotation, and session management for the Masterclass web application.
Schema validation, null-rate checks, and duration outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from modern single-page applications requires specific architectural choices. Here is how we build resilient pipelines.
Masterclass relies heavily on client-side rendering. Instead of scraping the visual DOM, our pipeline intercepts Next.js hydration state and internal API calls. This yields perfectly structured JSON directly from the source, bypassing UI changes.
High-volume requests from data centre IPs trigger Cloudflare blocks. Our crawlers use residential ISP proxies with realistic browser fingerprints to maintain access and retrieve localised pricing data.
When state parsing is unavailable, our selector strategy uses multiple fallback chains per field. We combine CSS selectors, XPath, and text-pattern matching so a layout change does not break your data pipeline.
For full catalogue monitoring, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing duration metrics, and coverage drops. We respond before you notice.
Online learning platforms monitor Masterclass course structures, lesson counts, and duration metrics to benchmark their own content production.
Content strategists analyse category saturation and new class releases to identify underserved topics in the premium education market.
Talent agencies and competing platforms track which experts are teaching specific subjects to map the premium instructor landscape.
Subscription businesses track Masterclass pricing tiers, promotional discounts, and regional adjustments to optimise their own pricing models.
Machine learning teams use structured curriculum hierarchies to train instructional design models and automated syllabus generators.
Private equity firms and analysts track catalogue growth velocity and category expansion to evaluate the premium EdTech sector.
"Masterclass defines premium online education. Mapping their curriculum provides the ultimate blueprint for high-production instructional design."
Extracting structured data from a modern React application requires more than simple HTTP requests. It demands state hydration parsing, proxy rotation, and resilient selectors. DataFlirt manages this complexity so your engineering team can focus on data modelling rather than pipeline maintenance.
Everything supported by our masterclass.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.
We bypass brittle DOM selectors by directly parsing the JSON state injected into the HTML by Next.js, ensuring high schema stability.
We maintain pools of residential ISP proxies to bypass Cloudflare protection and access regionally localised pricing data.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About masterclass.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Masterclass is generally permissible under applicable law. DataFlirt targets only public, non-authenticated course metadata, instructor profiles, and pricing data. We do not extract DRM-protected video content, user data, or bypass authentication walls.
No. Masterclass video content is DRM-protected and gated behind a paid subscription. We only extract public metadata, including trailer URLs, lesson titles, and duration metrics.
Masterclass is built with Next.js. Instead of relying on visual DOM selectors, our pipeline intercepts the initial hydration state embedded in the HTML. This provides clean, structured data directly from the backend API response.
For a site with the volume of Masterclass, we typically run full catalogue refreshes on a weekly or monthly cadence. The entire catalogue can be extracted in under two hours.
Yes. We use our residential proxy network to route requests through specific geographic regions, allowing us to capture localised subscription tiers and promotional pricing.
Our packages start at full catalogue extraction with monthly delivery. Contact us with your use case for a scoped quote based on delivery frequency and schema requirements.
Yes. We provide a sample run covering a subset of courses as part of the pre-engagement scoping process. This allows you to validate schema fit and field completeness before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous monitoring of new course releases. Tell us what you need.
