We extract school profiles, test scores, equity ratings, and community reviews from GreatSchools. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for School Profiles objects from greatschools.org. All fields typed and schema-versioned.
"school_id": "04128", "school_name": "Lincoln High School", "school_type": "Public", "grades_served": "9-12", "city": "Seattle", "state": "WA", "overall_rating": 8, "total_students": 1452
| # | school_id | school_name | school_type | grades_served | address | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Test Scores objects from greatschools.org. All fields typed and schema-versioned.
"school_id": "04128", "subject": "Math", "grade_level": "11", "proficiency_pct": 74, "state_avg_pct": 52, "test_year": "2023", "test_name": "Smarter Balanced Assessment"
| # | school_id | subject | grade_level | proficiency_pct | state_avg_pct | low_income_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Equity Ratings objects from greatschools.org. All fields typed and schema-versioned.
"school_id": "04128", "subgroup": "Hispanic", "equity_rating": 6, "graduation_rate": 88, "state_avg_grad_rate": 82, "suspension_rate": 2.1, "chronic_absenteeism": 14.5
| # | school_id | subgroup | equity_rating | graduation_rate | state_avg_grad_rate | college_readiness_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Community objects from greatschools.org. All fields typed and schema-versioned.
"review_id": "R948271", "school_id": "04128", "user_role": "Parent", "star_rating": 5, "review_text": "Excellent teachers and strong AP program.", "submitted_date": "2023-10-14", "helpful_votes": 12, "topic_category": "Academics"
| # | review_id | school_id | user_role | star_rating | review_text | submitted_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Demographics objects from greatschools.org. All fields typed and schema-versioned.
"school_id": "04128", "race_white_pct": 45, "race_asian_pct": 22, "race_hispanic_pct": 18, "race_black_pct": 8, "low_income_pct": 31, "student_teacher_ratio": 18, "free_lunch_pct": 28
| # | school_id | race_white_pct | race_black_pct | race_hispanic_pct | race_asian_pct | low_income_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our GreatSchools scraper captures every layer of the platform: overall ratings, detailed equity metrics, standardized test scores, and qualitative community feedback.
Address, district, grades served, and contact metadata for public, charter, and private schools.
Overall rating, test score rating, student progress rating, and equity rating extracted as integers.
Proficiency percentages by subject and grade, benchmarked against state averages.
Performance metrics broken down by race, ethnicity, and income levels.
AP course participation rates and STEM program availability.
Suspension rates, chronic absenteeism, and student-teacher ratios.
Star ratings and full text reviews from parents, students, and teachers across all pages.
Roll-up metrics for school districts and regional comparisons.
Track rating changes and new reviews across academic years with automated pipelines.
Brief in. Clean data out.
Provide state lists, zip codes, or district names. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for greatschools.org.
Schema validation, null-rate checks, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
GreatSchools employs dynamic rendering for its data visualizations. Here is how we extract the underlying numbers reliably.
GreatSchools renders test scores and demographic breakdowns using complex SVG charts. Instead of parsing DOM elements, we intercept the underlying JSON payloads powering these visualizations to guarantee exact percentage values.
We utilise US-based residential ISP proxies with realistic browser fingerprints to bypass perimeter defenses and prevent IP bans during full-state catalogue extractions.
School profiles often contain hundreds of paginated parent and student reviews. Our crawlers systematically traverse these endpoints without dropping records or triggering rate limits.
Public, private, and charter schools display different data modules. We use conditional extraction logic to normalise these structures into a single, predictable schema.
For ongoing monitoring, we maintain a hash index of last-seen values per school. Subsequent runs only push diffs, reducing downstream processing load.
Enriching property listings with local school ratings, test scores, and boundary data.
Benchmarking student performance and identifying district-level trends for product development.
Analysing equity gaps, demographic shifts, and educational outcomes across states.
Evaluating the impact of funding changes on standardised test scores and graduation rates.
Providing corporate transferees with detailed neighbourhood school profiles.
Assessing charter school networks and regional educational infrastructure.
"School quality is a primary driver of real estate value and demographic shifts. GreatSchools aggregates this reality, but requires infrastructure to query at scale."
Extracting data from GreatSchools involves parsing complex JavaScript visualisations, handling strict bot protection, and normalising fragmented district data. DataFlirt manages this pipeline end-to-end, delivering structured educational metrics directly to your warehouse so your analysts can focus on modelling.
Everything supported by our greatschools.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 for complex visualisations.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request to prevent rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About greatschools.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from GreatSchools is generally permissible. DataFlirt targets only public, non-authenticated school ratings, test scores, and reviews. We do not extract personal user data or circumvent authentication walls.
We use US residential ISP proxies and full Playwright browser sessions with realistic fingerprints. Our request timing is modelled on human behaviour to prevent triggering perimeter blocks.
We typically configure weekly or monthly cadences for educational data, aligning with academic cycles and standard reporting periods.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table for ratings and test scores from the date your pipeline starts.
Yes. We paginate across all parent, student, and teacher reviews, capturing full text, star ratings, and submission dates.
Yes. We provide a sample run of up to 500 schools as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off state extract or a continuous monitoring feed across the US, we scope, build, and operate the pipeline. Tell us what you need.
