We extract university profiles, financial aid details, scholarship databases, and graduate school programmes from Petersons. Delivered as clean JSON, CSV, or Parquet to your warehouse.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Undergraduate Colleges objects from petersons.com. All fields typed and schema-versioned.
"institution_id": "UG-10492", "name": "University of Michigan", "location_city": "Ann Arbor", "location_state": "MI", "acceptance_rate": 20.2, "tuition_in_state": 16736.0, "tuition_out_state": 55334.0, "enrollment_total": 48090
| # | institution_id | name | location_city | location_state | institution_type | acceptance_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Scholarships objects from petersons.com. All fields typed and schema-versioned.
"scholarship_id": "SCH-88391", "title": "Women in STEM Memorial Scholarship", "provider_name": "STEM Foundation", "award_amount": 5000.0, "deadline_date": "2025-04-15", "renewable": true, "number_of_awards": 10
| # | scholarship_id | title | provider_name | award_amount | deadline_date | academic_requirements |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Graduate Schools objects from petersons.com. All fields typed and schema-versioned.
"program_id": "GR-33920", "university_name": "Stanford University", "program_name": "Computer Science", "degree_type": "MS", "gre_required": false, "tuition_annual": 57300.0, "application_deadline": "2024-12-05"
| # | program_id | university_name | program_name | degree_type | department_name | gre_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Online Programmes objects from petersons.com. All fields typed and schema-versioned.
"listing_id": "ONL-9921", "institution_name": "Arizona State University", "program_title": "Information Technology", "degree_level": "BS", "cost_per_credit": 561.0, "total_credits": 120, "format": "100% Online"
| # | listing_id | institution_name | program_title | degree_level | format | duration_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Test Prep Metadata objects from petersons.com. All fields typed and schema-versioned.
"resource_id": "TP-4402", "test_name": "GRE", "category": "Quantitative Reasoning", "article_title": "Mastering Geometry for the GRE", "publish_date": "2023-08-14", "author": "Petersons Editorial", "tags": "['GRE', 'Math', 'Geometry']"
| # | resource_id | test_name | category | article_title | publish_date | author |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our infrastructure parses Petersons' deep search directories, normalising complex financial aid structures, acceptance statistics, and scholarship criteria into structured, queryable formats.
Extract core university data including location, institution type, student body demographics, and campus facilities.
Capture in-state versus out-of-state tuition fees, room and board costs, and average financial aid packages.
Track acceptance rates, yield rates, average SAT/ACT scores, and application deadlines across all institutions.
Parse award amounts, eligibility rules, demographic requirements, and renewal conditions for thousands of scholarships.
Extract degree types, department specifics, faculty ratios, and entrance exam requirements for grad schools.
Capture distance learning options, cost per credit hour, accreditation details, and programme duration.
Extract metadata for articles, guides, and study materials associated with SAT, ACT, GRE, and GMAT preparation.
Run pipelines on a weekly or monthly cadence to capture changing tuition costs and new scholarship deadlines.
We clean and standardise messy text fields into typed numerical values for immediate warehouse ingestion.
Brief in. Clean data out.
Specify target categories: undergraduate colleges, scholarships, or graduate programmes.
We configure Scrapy crawlers, manage pagination logic, and map the complex DOM structures.
Data types are enforced. Tuition strings become floats. Deadlines become ISO dates.
JSON, CSV, or Parquet delivered to your S3 bucket or Snowflake stage on schedule.
Extracting data from broad directory sites requires handling complex pagination, rate limiting, and inconsistent data formatting.
Petersons surfaces thousands of results per category. We manage cursor-based pagination and parameter manipulation to ensure 100% extraction coverage without missing records.
Tuition fees and scholarship amounts often appear as text ranges or mixed strings. Our pipeline cleans these into strict numeric fields during the extraction phase.
Directory scrapers often face IP bans. We use US-based residential proxies and enforce strict concurrency limits to maintain pipeline health.
Certain filter states and tab contents rely on client-side rendering. We deploy Playwright to execute JavaScript and capture the fully hydrated DOM.
Education portals frequently update their UI. We use multiple fallback selectors to ensure pipeline stability when Petersons alters their page layouts.
Aggregate college profiles and admission statistics to power student advisory and matching algorithms.
Build comprehensive scholarship search engines by ingesting award amounts and eligibility criteria.
Analyse tuition trends, acceptance rate shifts, and enrollment figures across different states and institution types.
Identify universities offering specific programmes to target marketing efforts for academic services.
Provide high school counsellors with up-to-date databases of college requirements and deadlines.
Track competitor university metrics including student-faculty ratios and demographic distributions.
"Petersons holds a massive catalogue of higher education data. Building a product on top of it requires structured extraction, not manual entry."
Parsing thousands of college profiles and scholarship rules requires robust pagination handling and strict data normalisation. DataFlirt manages the extraction infrastructure, delivering clean, typed data directly to your warehouse so your team can focus on application logic.
Everything supported by our petersons.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 combine Scrapy for high-throughput crawling with Playwright for rendering complex client-side applications.
Residential IPs ensure our requests blend with normal user traffic, avoiding rate limits and IP bans.
Airflow schedules extraction runs, while Kubernetes scales worker nodes based on target queue size.
Data delivered to where your team already works — no new tooling required.
About petersons.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public factual data such as tuition costs, acceptance rates, and scholarship details is generally permissible. We do not extract user personal data or bypass authentication for premium content. Clients must review their specific use cases against applicable terms of service.
Not all university profiles have complete data. Our schema enforces strict typing but allows nulls for missing fields. We flag high null rates in our observability stack to ensure it is a source issue and not a selector failure.
Yes. We configure the pipeline to start from specific search parameter URLs, limiting the extraction scope to exactly the data you require.
Education data changes seasonally. Most clients opt for monthly or quarterly full-catalogue refreshes, though weekly runs can be configured for scholarship deadlines.
Yes. We strip currency symbols, handle ranges, and output strict float values for immediate use in analytical queries.
We provide a sample dataset during the scoping phase to validate schema requirements and ensure the normalisation logic meets your standards.
20-minute scoping call. Pilot dataset within the week. Production within two. Specify your target universities, scholarships, or grad programmes. We build the pipeline and deliver structured data to your warehouse.
