We extract college directories, admission requirements, deadline schedules, essay prompts, and testing policies from Common App. 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 College Profiles objects from commonapp.org. All fields typed and schema-versioned.
"college_id": "CA_4921", "institution_name": "New York University", "city": "New York", "state": "NY", "institution_type": "Private", "campus_setting": "Urban", "undergrad_enrollment": 29312, "website_url": "https://www.nyu.edu"
| # | college_id | institution_name | city | state | zip_code | institution_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Application Deadlines objects from commonapp.org. All fields typed and schema-versioned.
"college_id": "CA_4921", "term": "Fall 2025", "admission_plan": "Early Decision I", "deadline_date": "2024-11-01", "decision_date": "2024-12-15", "application_fee_usd": 80.0, "international_fee": 80.0, "fee_waiver_available": true
| # | college_id | term | admission_plan | deadline_date | decision_date | application_fee_usd |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Essay Prompts objects from commonapp.org. All fields typed and schema-versioned.
"college_id": "CA_4921", "prompt_id": "PR_882", "prompt_text": "We would like to know more about your interest in NYU. What motivated you to apply?", "min_words": 0, "max_words": 400, "is_required": true, "program_specific": false, "term": "Fall 2025"
| # | college_id | prompt_id | prompt_text | min_words | max_words | is_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Testing Policies objects from commonapp.org. All fields typed and schema-versioned.
"college_id": "CA_4921", "sat_act_policy": "Test Optional", "test_blind": false, "self_report_allowed": true, "english_proficiency_required": true, "toefl_min_score": 100, "ielts_min_score": 7.5, "duolingo_min_score": 130
| # | college_id | sat_act_policy | test_blind | self_report_allowed | english_proficiency_required | toefl_min_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Recommendations objects from commonapp.org. All fields typed and schema-versioned.
"college_id": "CA_4921", "counselor_eval_required": true, "teacher_eval_min": 1, "teacher_eval_max": 2, "other_eval_min": 0, "other_eval_max": 1, "mid_year_report_required": true, "final_report_required": true
| # | college_id | counselor_eval_required | teacher_eval_min | teacher_eval_max | other_eval_min | other_eval_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Common App scraper parses the entire directory of member institutions, capturing granular admission requirements, deadlines, and supplementary materials ahead of every application cycle.
Extract core institution metadata including campus setting, enrollment figures, institution type, and official web properties for all member colleges.
Map Early Decision, Early Action, Regular Decision, and Rolling admission deadlines across Fall, Spring, and Summer terms.
Capture institution-specific writing supplements, short answers, and portfolio requirements including word count limits and program dependencies.
Monitor SAT, ACT, and English proficiency testing policies. Track shifts between test-optional, test-blind, and test-required mandates.
Extract domestic and international application fees, standardized test fee policies, and fee waiver availability per institution.
Quantify exact requirements for counselor evaluations, teacher recommendations, and optional supplementary letters.
Separate extraction schemas for first-year applicant requirements versus transfer applicant prerequisites.
Identify and alert on schema and requirement changes immediately following the annual August 1 application cycle rollover.
Receive normalised requirement matrices in CSV for analysts or nested JSON for engineering teams.
Brief in. Clean data out.
Provide specific institution lists or request a full directory scrape. We map the required data fields together.
We configure Playwright crawlers to handle Common App's single-page architecture and extract nested requirement data.
Schema validation, null-rate checks, and requirement logic verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting from commonapp.org requires navigating single-page application hydration and frequent layout shifts during the admission cycle rollover. Here is how we maintain stability.
Common App relies heavily on client-side rendering. We run full Playwright browser sessions to ensure all asynchronous data fetching completes before extraction, capturing requirement accordions that headless HTTP clients miss entirely.
The platform frequently updates its DOM structure, especially around the August 1 cycle reset. Our selector strategy uses fallback chains and API interception to ensure your data pipeline survives layout changes.
We utilise residential ISP proxies with realistic browser fingerprints and randomised request timing to prevent IP bans and rate limiting during high-volume directory sweeps.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs, providing a clean changelog of deadline extensions or policy updates rather than full re-dumps.
Every run emits structured logs. We alert on null-rate spikes, missing essay prompts, and coverage drops, responding before downstream systems are affected.
College discovery and application management platforms ingest Common App data to keep their internal deadline and requirement trackers accurate.
Independent counselors and high school advisory networks use extracted essay prompts to build curriculum and writing guides ahead of the admission season.
University enrollment offices benchmark their deadlines, fees, and testing policies against peer and aspirant institutions.
Financial aid platforms map institutional merit scholarship requirements against Common App deadlines to alert students of upcoming opportunities.
Tutoring networks track the shift in test-optional and test-blind policies to forecast demand for SAT and ACT preparation services.
Data science teams correlate application requirements and fee structures with historical yield rates to refine predictive admission models.
"Common App centralises the admission requirements for over a thousand universities, but accessing this data programmatically requires constant pipeline maintenance."
Most teams underestimate the investment required: reliable commonapp.org scraping requires handling JavaScript-heavy single-page applications, dynamic routing, and frequent layout changes ahead of application cycles. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our commonapp.org 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 retry logic. Playwright handles JavaScript rendering and interaction flows required for Common App's single-page architecture.
We maintain pools of residential ISP proxies to ensure reliable extraction without triggering rate limits or IP blocks during high-volume directory sweeps.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About commonapp.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information, deadlines, and application requirements is generally permissible. DataFlirt targets only public, non-authenticated institutional data. We explicitly do not extract student profiles, submitted applications, or any FERPA-protected personal data. Clients should review Common App terms of service and consult legal counsel for specific use cases.
Common App resets its system annually on August 1, often accompanied by significant DOM and schema changes. We schedule intensive pipeline maintenance windows around this date, updating selectors and API interception logic to ensure data flows resume immediately for the new cycle.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per institution, allowing you to track when a college shifts from test-optional to test-required.
Yes. We capture base institutional writing supplements as well as prompts conditionally required by specific majors, honors programs, or scholarship applications.
For full directory sweeps, we typically run pipelines on a weekly or daily cadence depending on your requirements. Changes to deadlines or policies are captured within the next scheduled run.
Our baseline package includes full extraction of the ~1,000+ member college directory, including deadlines and testing policies, delivered weekly. Contact us for custom cadences or specific sub-schemas.
Absolutely. We provide a sample run of 50 institution profiles, including their associated deadlines and essay prompts, so you can validate schema fit and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off directory export or continuous tracking of admission requirements across 1,000+ institutions, we scope, build, and operate the pipeline. Tell us what you need.
