We extract class listings, educator profiles, scheduling data, pricing, and learner reviews from Outschool. 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 Class Listings objects from outschool.com. All fields typed and schema-versioned.
"class_id": "cls_8f92j1k", "title": "Introduction to Python Programming for Teens", "educator_name": "Sarah Jenkins", "format": "Live Online", "age_min": 13, "age_max": 17, "price": 120.0, "rating": 4.9, "review_count": 342
| # | class_id | title | summary | educator_id | educator_name | subject |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Educator Profiles objects from outschool.com. All fields typed and schema-versioned.
"educator_id": "edu_99x2m4", "name": "Sarah Jenkins", "headline": "Former Software Engineer teaching coding", "total_reviews": 1205, "total_learners": 4500, "total_classes": 18, "average_rating": 4.9, "joined_date": "2021-04-12"
| # | educator_id | name | headline | bio | total_reviews | total_learners |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Schedules & Availability objects from outschool.com. All fields typed and schema-versioned.
"schedule_id": "sch_77b3n1", "class_id": "cls_8f92j1k", "start_date": "2026-06-01", "end_date": "2026-06-22", "available_seats": 2, "total_seats": 10, "status": "Enrolling", "price": 120.0
| # | schedule_id | class_id | start_date | end_date | meeting_times | timezone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from outschool.com. All fields typed and schema-versioned.
"review_id": "rev_44k9p2", "class_id": "cls_8f92j1k", "reviewer_name": "Michelle T.", "rating": 5, "review_text": "My son loved this class and built his first game.", "date_posted": "2026-05-10", "learner_age": 14, "helpful_votes": 12
| # | review_id | class_id | educator_id | reviewer_name | rating | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from outschool.com. All fields typed and schema-versioned.
"keyword": "python coding", "position": 3, "class_id": "cls_8f92j1k", "title": "Introduction to Python Programming for Teens", "price": 120.0, "rating": 4.9, "format_badge": "Live", "scraped_at": "2026-05-12T10:15:00Z"
| # | keyword | position | class_id | title | educator_name | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Outschool scraper handles the dynamic nature of the platform: live schedule availability, educator metrics, nested subject categories, and paginated reviews with strict schema validation.
Extract titles, descriptions, format types, age ranges, meeting counts, and pricing for every published class.
Monitor cohort start dates, meeting times, available seats, and enrollment status across multiple timezones.
Capture educator biographies, total learner counts, aggregate review scores, and historical class listings.
Extract full review text, star ratings, learner ages, and educator replies across all class pages.
Monitor class positions for specific subject keywords to understand marketplace visibility.
Map classes to Outschool's internal category tree from high-level subjects down to specific topics.
Track price per hour, subscription pricing, and one-time fees across Live, Flex, and Group formats.
Identify new class launches, price adjustments, and schedule additions without processing redundant data.
Run pipelines daily to capture fast-moving seat availability and schedule changes.
Brief in. Clean data out.
Provide subject categories, educator IDs, or search keywords. We design the extraction schema together.
We configure Scrapy crawlers, handle React hydration for schedules, and manage proxy rotation.
Schema validation, null-rate checks, and schedule alignment verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
EdTech marketplaces rely heavily on client-side rendering for scheduling and availability. Here is how we ensure data completeness.
Outschool class schedules and available seat counts load dynamically via JavaScript after the initial page request. We use Playwright to execute the JavaScript context, wait for network idle states, and extract the fully rendered schedule matrices.
Class times are displayed based on the requesting IP's local timezone. Our infrastructure normalises all extracted meeting times to UTC, preventing data corruption when proxies route through different geographic locations.
Search results and category pages often cap visible results. We bypass these arbitrary UI limits by iterating through the underlying API endpoints and filtering by granular age ranges to ensure 100% catalogue coverage.
Frequent requests to educator profiles and class listings trigger rate limits. We distribute requests across a pool of US-based residential proxies, maintaining low concurrency per IP to avoid detection and ensure uninterrupted extraction.
Marketplaces frequently A/B test their class detail pages. We extract primary data directly from the Next.js application state (__NEXT_DATA__) rather than relying solely on fragile DOM selectors.
EdTech platforms benchmark their class pricing, subscription models, and hourly rates against Outschool averages.
Content creators analyze class volume and review counts across subjects to identify underserved topics.
Marketplace operators monitor the growth of active educators and their class output to understand supply-side dynamics.
Analysts correlate available seats with total seats over time to estimate enrollment velocity and gross marketplace value.
Strategy teams track the shift between Live Online, Flex, and Group classes to align their own product roadmaps.
Private equity firms analyze review sentiment, pricing trends, and catalogue size to evaluate marketplace health.
"Outschool holds the definitive dataset on supplementary K-12 online education, but mapping dynamic schedules to static records requires dedicated infrastructure."
Most teams underestimate the complexity of scraping ed-tech marketplaces. Outschool relies on heavy client-side rendering for schedule availability and localised pricing. DataFlirt handles the JavaScript execution, proxy rotation, and schema normalisation so your engineers can focus on analysis rather than maintenance.
Everything supported by our outschool.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 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 outschool.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated class, educator, and schedule data. We do not extract personal learner data or circumvent authentication walls.
We use headless browsers via Playwright to execute the client-side React code, allowing the schedule components to fully hydrate before extraction.
Yes. By running pipelines on a daily cadence, we capture the available seat count for specific class cohorts, allowing you to calculate enrollment velocity.
We extract all public data on the educator profile, including their bio and aggregate metrics. We do not extract private email addresses or direct messaging endpoints.
Pipelines can be configured to run daily or sub-daily depending on your requirements. Change-detection ensures you only process updated schedules.
Yes. We provide a sample run of up to 500 classes as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous schedule monitoring across thousands of classes, we build and operate the pipeline. Tell us what you need.
