We extract course catalogues, curriculum metadata, pricing plans, and instructor intelligence from Teachable storefronts. 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 teachable.com. All fields typed and schema-versioned.
"course_id": "crs_8921x", "title": "Advanced Python Data Engineering", "subtitle": "Build scalable data pipelines from scratch", "instructor_name": "Jane Doe", "price_min": 199.0, "currency": "USD", "enrollment_status": "open", "storefront_url": "https://courses.janedoe.com/p/data-engineering"
| # | course_id | title | subtitle | instructor_name | category | price_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing Plans objects from teachable.com. All fields typed and schema-versioned.
"course_id": "crs_8921x", "plan_id": "pln_441a", "plan_name": "Lifetime Access", "plan_type": "one_time", "price": 199.0, "currency": "USD", "installments": 1, "is_active": true
| # | course_id | plan_id | plan_name | plan_type | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Curriculum Structure objects from teachable.com. All fields typed and schema-versioned.
"course_id": "crs_8921x", "module_name": "Module 1: Infrastructure", "lesson_title": "Setting up AWS IAM", "is_preview": true, "content_type": "video", "duration_seconds": 845, "order_index": 3
| # | course_id | module_id | module_name | lesson_id | lesson_title | is_preview |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructor Profiles objects from teachable.com. All fields typed and schema-versioned.
"instructor_id": "inst_77b2", "name": "Jane Doe", "bio": "Ex-FAANG Data Engineer teaching modern data stacks.", "avatar_url": "https://cdn.teachable.com/avatars/77b2.jpg", "social_links": "['https://twitter.com/janedoe']", "total_courses": 4, "school_name": "Data Engineering Academy"
| # | instructor_id | name | bio | avatar_url | social_links | total_courses |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sales Page Copy objects from teachable.com. All fields typed and schema-versioned.
"course_id": "crs_8921x", "headline": "Master the Modern Data Stack", "target_audience": "Software engineers transitioning to data roles", "requirements": "['Basic Python', 'SQL fundamentals']", "testimonials": 12, "scraped_at": "2026-05-12T09:14:33Z"
| # | course_id | headline | description_html | target_audience | requirements | faq_json |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Teachable scraper handles the platform's custom domain mapping, heavily customised storefront themes, dynamic pricing widgets, and curriculum structures — delivering normalised data regardless of how the creator configured their school.
Extract module names, lesson titles, content types, duration metadata, and free preview flags across the entire course syllabus.
Capture one-time payments, subscriptions, payment plans, and bundle pricing accurately, normalising currencies and billing intervals.
Scrape instructor names, biographies, social links, and cross-reference multiple courses taught by the same creator.
Automatically identify and map creators using custom domains back to the underlying Teachable infrastructure for consistent extraction.
Extract headlines, HTML descriptions, FAQs, and testimonials from highly customised sales pages using NLP heuristic matching.
Identify when courses are sold as bundles and map the parent-child relationships between individual courses and the bundle package.
Extract pricing data across all supported local currencies, maintaining exact price points and currency codes.
Crawl entire Teachable schools to discover unlisted or newly published courses automatically.
Run continuous pipelines to monitor for pricing changes, new course launches, or syllabus updates with hash-based diffing.
Brief in. Clean data out.
Provide Teachable school URLs, custom domains, or instructor names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and custom domain resolution logic.
Schema validation, null-rate checks, and pricing accuracy verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from a platform designed for extreme customisation requires adaptive parsing. Here is how we normalise fragmented storefronts.
Many top creators use custom domains (e.g., courses.creator.com) rather than teachable.com subdomains. Our pipeline identifies underlying Teachable footprints via HTTP headers and specific DOM structures, allowing us to aggregate data across thousands of independent domains into a single normalised dataset.
Teachable allows creators to heavily modify their sales pages using custom HTML/CSS blocks. We use heuristic parsing and XPath fallback chains to reliably identify pricing widgets, curriculum lists, and instructor bios regardless of the visual theme applied.
Pricing tiers and checkout links are often loaded dynamically via JavaScript based on geo-location or active promotions. We use Playwright to execute these scripts, capturing the true rendered price rather than stale server-side HTML.
Scraping an entire school's catalogue rapidly triggers rate limits. We distribute requests across residential IP pools, managing concurrency and request delays to ensure complete extraction without triggering defensive blocks.
For large course catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load. You get a clean changelog rather than full re-dumps.
MCNs, agencies, and brands identify successful course creators for partnership opportunities based on catalogue size and pricing tiers.
EdTech platforms and independent creators monitor competitor pricing models, subscription vs one-time ratios, and bundle strategies.
Analysts track trending course topics, curriculum density, and category saturation to identify whitespace in the eLearning market.
Review sites and course aggregators build search indexes by normalising metadata across thousands of independent Teachable schools.
LLM developers use structured syllabus data (modules, lesson titles, sequencing) to train educational planning and curriculum generation models.
B2B SaaS companies targeting the creator economy build highly qualified prospect lists based on course volume and pricing tiers.
"Teachable hosts a massive share of the independent creator economy — but fragmented custom domains make aggregating this curriculum data an infrastructure nightmare."
Most teams struggle with Teachable's custom domain mapping and highly customisable storefront themes. DataFlirt absorbs that complexity, handling domain resolution, dynamic pricing extraction, and anti-bot circumvention so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our teachable.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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US/UK/EU regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About teachable.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Teachable storefronts is generally permissible. DataFlirt targets only public, non-authenticated course metadata, pricing, and curriculum structures. We do not extract gated paid content, student PII, or circumvent authentication walls. Clients should consult legal counsel for specific use cases.
Yes. Our pipeline identifies the underlying Teachable infrastructure via network fingerprints, allowing us to scrape and normalise data from custom domains (e.g., courses.creator.com) exactly as we would from a teachable.com subdomain.
No. We only extract the public-facing curriculum structure (module names, lesson titles, duration, and free preview status). The actual paid content remains behind a login wall and is not supported.
Teachable allows heavy theme customisation. We use heuristic parsing, NLP matching, and multi-layer XPath fallback chains to identify pricing widgets, instructor bios, and FAQs regardless of the specific visual theme applied by the creator.
For continuous monitoring, pipelines can run daily or weekly to detect new course launches and pricing updates. Full catalogue refreshes complete within a few hours depending on the target list size.
Absolutely. We provide a sample run of up to 50 Teachable schools as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off course catalogue dump or a continuous pricing feed across thousands of creator domains — we scope, build, and operate the pipeline. Tell us what you need.
