We extract resource listings, pricing signals, seller intelligence, grade-level alignments, and reviews from Teachers Pay Teachers. 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 Resource Listings objects from teacherspayteachers.com. All fields typed and schema-versioned.
"resource_id": "8472911", "title": "Fractions Worksheets - 4th Grade Math", "seller_name": "Math Masters", "price": 4.5, "grade_levels": "['4th', '5th']", "rating": 4.9, "review_count": 342, "page_count": 25
| # | resource_id | title | seller_id | seller_name | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Seller Data objects from teacherspayteachers.com. All fields typed and schema-versioned.
"seller_id": "928374", "store_name": "Science Squad Resources", "follower_count": 14205, "total_resources": 412, "average_rating": 4.8, "join_date": "2018-04-12"
| # | seller_id | store_name | store_url | follower_count | total_resources | free_resources |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from teacherspayteachers.com. All fields typed and schema-versioned.
"review_id": "RV-9928174", "resource_id": "8472911", "rating": 5, "review_text": "Perfect for my intervention group. Highly engaging.", "date_posted": "2023-11-04", "buyer_type": "Teacher"
| # | review_id | resource_id | reviewer_name | rating | review_text | date_posted |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Standards Alignment objects from teacherspayteachers.com. All fields typed and schema-versioned.
"resource_id": "8472911", "standard_type": "Common Core", "standard_code": "CCSS.MATH.CONTENT.4.NF.A.1", "standard_description": "Explain why a fraction a/b is equivalent to a fraction...", "grade": "4", "domain": "Number & Operations - Fractions"
| # | resource_id | standard_type | standard_code | standard_description | grade | subject_area |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Search Results objects from teacherspayteachers.com. All fields typed and schema-versioned.
"keyword": "reading comprehension 3rd grade", "position": 3, "resource_id": "552910", "title": "Reading Passages with Questions", "price": 8.0, "is_sponsored": false, "is_bundle": true
| # | keyword | position | resource_id | title | seller_name | price |
|---|---|---|---|---|---|---|
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Our pipeline handles the complex React rendering and deep taxonomy trees of Teachers Pay Teachers, extracting resources, seller metrics, and standards alignments at scale.
Extract titles, descriptions, prices, page counts, formats, and preview image URLs for every listing.
Track store follower counts, total inventory size, average ratings, and featured items across thousands of sellers.
Capture highly nested grade levels, subject areas, and resource types mapped exactly as they appear.
Extract Common Core and state-specific standard codes linked to individual educational resources.
Paginate through resource reviews to capture buyer sentiment, ratings, dates, and helpful votes.
Map parent-child relationships between resource bundles and their individual component listings.
Monitor list prices, active sale discounts, and site-wide promotional pricing events.
Monitor keyword positions to differentiate between organic ranking and sponsored placements.
Run continuous pipelines that only emit records when prices, follower counts, or resource details change.
Brief in. Clean data out.
Provide seller URLs, category parameters, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for teacherspayteachers.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Educational marketplaces rely on complex JavaScript frameworks and aggressive bot protection. Here is how we maintain stable extraction.
Teachers Pay Teachers uses advanced bot mitigation and WAF rules. Our crawlers use US-based residential proxies combined with realistic browser headers and TLS fingerprint spoofing to maintain access without triggering blocks.
TpT relies heavily on client-side React rendering. We execute full Playwright sessions to allow data hydration, ensuring we capture dynamic pricing, reviews, and preview components that static HTTP requests miss.
Marketplaces often cap search pagination at 100 pages. We bypass this limitation by algorithmically generating deep filter combinations (by price, grade, and subject) to extract the entire catalogue.
Grade levels and subjects are highly nested. We parse and normalise this taxonomy into flat, queryable arrays so your analysts can group data by specific grades or micro-subjects without writing complex parsing logic.
For large seller catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Publishers analyze gaps in grade and subject coverage to identify high-demand, low-supply curriculum opportunities.
Top sellers track competitor pricing, bundle strategies, and new product launches to optimise their own storefronts.
Analysts track historical discount strategies and price elasticity across different resource formats to maximise revenue.
Marketing teams monitor keyword search volumes and seasonal resource demand (e.g., back-to-school trends).
EdTech platforms identify top-performing sellers and resource creators for partnership and acquisition outreach.
Creators correlate review volume and ratings with specific standard alignments to guide future resource development.
"Teachers Pay Teachers holds the definitive taxonomy of educator demand, but extracting structured curriculum alignments requires custom pipeline engineering."
Most teams underestimate the complexity of scraping educational marketplaces. Reliable extraction requires handling complex React state, bypassing aggressive bot protection, and normalising highly nested taxonomy trees for standards and grades. DataFlirt absorbs that infrastructure burden so you can focus on analysis.
Everything supported by our teacherspayteachers.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 IN/US/UK/DE 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 teacherspayteachers.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated resource listings, seller metrics, and reviews. We do not bypass paywalls to download gated PDF or ZIP files.
We use US-based residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and automated solver integrations. We monitor for block rates in real time and rotate IP pools automatically.
Yes. We extract all listed educational standards, including Common Core and state-specific codes, linking them directly to the resource ID in the output schema.
TpT limits search result visibility to a set number of pages. We bypass this by programmatically generating deep filter permutations across grades, subjects, and price tiers to extract the entire catalogue.
Pipelines can be configured for daily, weekly, or monthly cadences. Change detection ensures that only updated records are processed and delivered, reducing latency.
Yes. We provide a sample run of up to 500 resources or 50 seller profiles during the scoping process so you can validate the schema and data quality before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous tracking across thousands of sellers — we scope, build, and operate the pipeline. Tell us what you need.
