We extract programme curricula, tuition fees, application deadlines, and admission criteria from Studyportals. 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 Degree Programmes objects from studyportals.eu. All fields typed and schema-versioned.
"programme_id": "SP-847291", "title": "Data Science and Artificial Intelligence", "degree_type": "MSc", "university_name": "Technical University of Munich", "location_city": "Munich", "location_country": "Germany", "duration_months": 24, "language_of_instruction": "English"
| # | programme_id | title | degree_type | university_name | location_city | location_country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Admission Requirements objects from studyportals.eu. All fields typed and schema-versioned.
"programme_id": "SP-847291", "ielts_min_score": 6.5, "toefl_min_score": 90, "gpa_minimum": "3.0/4.0", "work_experience_required": false, "interview_required": true, "gre_gmat_required": false
| # | programme_id | academic_requirement_text | ielts_min_score | toefl_min_score | gpa_minimum | work_experience_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for University Profiles objects from studyportals.eu. All fields typed and schema-versioned.
"university_id": "U-1048", "name": "Technical University of Munich", "country": "Germany", "city": "Munich", "global_rank_qs": 37, "student_count": 50467, "intl_student_pct": 38
| # | university_id | name | country | city | global_rank_the | global_rank_qs |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tuition & Funding objects from studyportals.eu. All fields typed and schema-versioned.
"programme_id": "SP-847291", "fee_domestic": 0.0, "fee_international": 3000.0, "currency": "EUR", "payment_cycle": "per semester", "living_cost_estimate": 1200.0, "scholarships_available": true
| # | programme_id | fee_domestic | fee_international | currency | payment_cycle | living_cost_estimate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Deadlines & Dates objects from studyportals.eu. All fields typed and schema-versioned.
"programme_id": "SP-847291", "intake_month": "October", "application_deadline": "2025-05-31", "term_start_date": "2025-10-01", "duration_months": 24, "study_mode": "Full-time"
| # | programme_id | intake_month | application_deadline | late_app_deadline | term_start_date | term_end_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Studyportals scraper extracts global education data across thousands of universities. We handle currency normalisation, location-based rendering, and unstructured admission criteria parsing.
Title, degree type, curriculum structure, duration, and study mode extracted for Bachelor, Master, and PhD programmes globally.
Capture domestic and international tuition fees, payment cycles, and living cost estimates across multiple base currencies.
Extract IELTS, TOEFL, GRE, and GPA requirements from unstructured text blocks into strict numeric fields.
Scrape university profiles, campus locations, student demographics, and global ranking metrics (QS, THE).
Monitor application deadlines, intake months, and term start dates for both EU and non-EU applicants.
Studyportals alters fees and deadlines based on visitor IP. We use specific regional proxies to capture exact data for target applicant demographics.
Map programmes to standard academic disciplines and sub-disciplines for accurate taxonomy building.
Filter and extract specific cohorts of online degrees, short courses, and blended learning programmes.
Run continuous pipelines to capture new programme launches and tuition fee updates ahead of major intake seasons.
Brief in. Clean data out.
Provide target disciplines, countries, or specific university lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy routing for accurate fee rendering, and pagination logic.
Schema validation, null-rate checks, currency outlier detection, and language parsing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting higher education data requires precise geo-routing and unstructured text parsing. Here is how we maintain pipeline integrity.
Studyportals dynamically displays tuition fees and deadlines based on the visitor's IP address (EU vs non-EU). Our pipeline forces specific residential proxy regions to extract the correct fee structures for your target applicant demographic.
Language requirements and GPA minimums are often buried in unstructured text blocks. We deploy regex and NLP parsing to extract exact IELTS scores, TOEFL minimums, and credit requirements into clean numeric fields.
Broad discipline searches yield thousands of results across deep pagination structures. We bypass UI limitations by interacting directly with the underlying JSON APIs where available, ensuring zero dropped records.
Platform layouts change frequently. Our selector strategy uses multiple fallback chains per field, ensuring a CSS class update does not break your data pipeline overnight.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in critical fields like tuition fees or deadlines, pausing delivery until the schema is patched.
Student recruitment platforms ingest our feeds to populate their own programme discovery engines without manual data entry.
Academic institutions track competitor tuition fees, new programme launches, and curriculum structures to benchmark their own offerings.
Lenders and scholarship platforms use tuition and living cost data to model loan products and assess funding requirements.
International student recruitment agencies map admission criteria against student profiles to automate application shortlisting.
Policy makers analyse global mobility trends, discipline popularity, and international fee structures to inform domestic education policy.
Machine learning teams train career and education matching algorithms on structured curriculum and outcome data.
"Studyportals aggregates the global higher education market, but mapping thousands of fragmented admission criteria into a unified schema requires dedicated infrastructure."
Most teams underestimate the complexity of education data extraction. Parsing unstructured IELTS requirements, normalising tuition fees across 40 currencies, and managing deep pagination requires continuous maintenance. DataFlirt absorbs that complexity so your engineers can focus on product development.
Everything supported by our studyportals.eu 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. Rotation happens per-request with strict geographic targeting to ensure accurate tuition fee rendering for specific applicant cohorts.
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 studyportals.eu scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Studyportals is generally permissible under applicable law. DataFlirt targets only public, non-authenticated degree programme, tuition, and university data. We do not extract personal user data or circumvent authentication walls. Clients should review target site ToS and consult legal counsel for specific use cases.
Studyportals alters fee displays based on IP geolocation to distinguish between EU and non-EU applicants. We configure our residential proxy pools to route requests through the specific country you require, guaranteeing accurate fee extraction.
Yes. The Studyportals network operates on a shared underlying architecture. We can configure a unified pipeline that extracts data across all their sub-domains into a single normalised schema.
We use a combination of regex patterns and NLP to parse text blocks like 'Applicants need a minimum IELTS score of 6.5 with no band below 6.0' into discrete numeric fields in your database.
For full catalogue extractions spanning hundreds of thousands of programmes, we typically run weekly or monthly cadences. Targeted updates for specific universities or disciplines can be scheduled daily.
Our smallest packages start at a defined list of universities or a specific discipline cluster with monthly delivery. For full global catalogue extraction, we price based on compute volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 programmes as part of the pre-engagement scoping process, allowing you to validate schema fit and parsing accuracy before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed of tuition updates across 100K programmes - we scope, build, and operate the pipeline. Tell us what you need.
