We extract scholarship details, college statistics, admission criteria, and student reviews from Unigo. 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 Scholarships objects from unigo.com. All fields typed and schema-versioned.
"scholarship_id": "SCH-8921", "title": "STEM Excellence Award", "provider": "Tech Foundation", "award_amount": 5000, "deadline": "2026-03-15", "category": "Engineering", "number_of_awards": 10
| # | scholarship_id | title | provider | award_amount | deadline | eligibility_criteria |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for College Profiles objects from unigo.com. All fields typed and schema-versioned.
"unitid": "193900", "name": "New York University", "location": "New York, NY", "institution_type": "Private", "total_enrollment": 52885, "tuition_in_state": 56500, "acceptance_rate": 0.16
| # | unitid | name | location | institution_type | total_enrollment | tuition_in_state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Student Reviews objects from unigo.com. All fields typed and schema-versioned.
"review_id": "REV-449102", "college_id": "193900", "author_year": "Junior", "overall_rating": 4.5, "academics_rating": 5.0, "review_text": "Great professors and networking opportunities.", "date_posted": "2025-11-02"
| # | review_id | college_id | author_year | overall_rating | academics_rating | campus_life_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Admissions Data objects from unigo.com. All fields typed and schema-versioned.
"college_id": "193900", "application_deadline": "2026-01-01", "application_fee": 80, "sat_reading_25th": 680, "sat_math_25th": 700, "high_school_gpa_req": "Required", "essay_req": "Required"
| # | college_id | application_deadline | application_fee | sat_reading_25th | sat_math_25th | act_composite_25th |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financial Aid objects from unigo.com. All fields typed and schema-versioned.
"college_id": "193900", "avg_net_price": 42397, "pct_receiving_aid": 0.52, "avg_grant_amount": 32000, "pell_grant_pct": 0.18, "institutional_grant_pct": 0.48, "work_study_available": true
| # | college_id | avg_net_price | pct_receiving_aid | avg_grant_amount | avg_loan_amount | pell_grant_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Unigo scraper handles dynamic search filters, pagination state, and nested review components to deliver clean, relational data for colleges and scholarships.
Capture award amounts, deadlines, eligibility criteria, and descriptions across thousands of scholarship listings.
Extract tuition costs, enrollment figures, acceptance rates, and demographic breakdowns for every listed institution.
Scrape student review text, star ratings, and sub-ratings for academics, campus life, and dorms.
Collect standardized test score percentiles, application deadlines, and high school GPA requirements.
Extract average net price, grant percentages, and typical student debt figures per institution.
Monitor scholarship deadlines and new review submissions with scheduled daily or weekly pipeline runs.
Apply specific search parameters to target scholarships by major, state, or demographic eligibility.
Navigate deep pagination structures to ensure complete capture of review corpora and directory listings.
Standardise date formats, currency values, and percentage figures into strict typed schemas.
Brief in. Clean data out.
Provide target categories, scholarship filters, or college lists. We design the extraction schema together.
We configure Scrapy crawlers, handle pagination state, and manage IP rotation for unigo.com.
Schema validation, null-rate checks, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from dynamic directories requires specific infrastructure. Here is how we manage the pipeline.
Unigo relies on JavaScript to render scholarship lists and review pagination. We use Playwright to execute client-side code and capture the fully hydrated DOM.
We implement multi-layered XPath and CSS selectors to handle variations in college profile layouts and missing data fields.
To maintain high concurrency without triggering rate limits, we route requests through US-based residential proxy networks.
For daily scholarship updates, we hash existing records and only emit new or modified listings, reducing downstream processing costs.
We intercept background API calls and parse embedded Next.js data objects to extract structured information before it renders.
Incorporate scholarship directories and college statistics into student advisory applications.
Identify institutions with specific demographic profiles for targeted marketing campaigns.
Analyse student sentiment trends across different institution types using the review corpus.
Incorporate tuition costs and financial aid statistics into student loan underwriting models.
Build internal databases of admission requirements and scholarship deadlines for client advising.
Track changes in tuition costs and acceptance rates across state university systems.
"Unigo contains the most comprehensive database of private scholarships and verified student reviews, but accessing it systematically requires dedicated pipeline infrastructure."
Most teams underestimate the investment required: reliable Unigo scraping requires handling dynamic React components, pagination state, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our unigo.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 and deduplication. Playwright handles JavaScript rendering and interaction flows for dynamic directories.
We maintain pools of residential ISP proxies. Rotation happens per-request to ensure high concurrency without IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About unigo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Unigo is generally permissible. DataFlirt targets only public, non-authenticated scholarship, college, and review data. We do not extract personal user data or circumvent authentication walls.
We use Playwright to execute the underlying JavaScript pagination logic, ensuring we capture the complete dataset across all directory pages without missing records.
We can configure pipelines to run daily, capturing new scholarship listings and updating approaching deadlines within 24 hours of publication.
Yes. We can apply specific search parameters to target scholarships by major, demographic, state, or award amount based on your requirements.
Yes. We paginate through the entire review history for specified colleges, capturing the full text, date, and sub-category ratings.
Our minimum engagement typically starts at a defined set of colleges or scholarship categories with weekly delivery. Contact us for a scoped quote based on your volume.
Yes. We provide a sample run of up to 100 college profiles or 500 scholarship listings to validate schema fit and data quality before contract signing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of college statistics or a continuous feed of scholarship deadlines, we build and operate the pipeline. Tell us what you need.
