We extract US school rankings, district boundaries, demographic shifts, and historical test scores from SchoolDigger. 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 School Profiles objects from schooldigger.com. All fields typed and schema-versioned.
"nces_id": "062271003230", "school_name": "Lowell High School", "district_name": "San Francisco Unified", "state": "CA", "school_type": "Public", "total_enrollment": 2841, "student_teacher_ratio": 22.4, "rank_current": 14
| # | nces_id | school_name | district_name | state | school_type | grades_served |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for District Data objects from schooldigger.com. All fields typed and schema-versioned.
"district_id": "0622710", "district_name": "San Francisco Unified", "state": "CA", "total_schools": 113, "total_students": 53928, "rank_current": 218, "expenditure_per_student": 17492.0
| # | district_id | district_name | state | total_schools | total_students | rank_current |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Test Scores objects from schooldigger.com. All fields typed and schema-versioned.
"school_id": "062271003230", "year": 2025, "grade_level": "11", "subject": "Mathematics", "proficiency_pct": 88.4, "state_average": 34.2, "test_name": "CAASPP"
| # | school_id | year | grade_level | subject | proficiency_pct | state_average |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Demographics objects from schooldigger.com. All fields typed and schema-versioned.
"school_id": "062271003230", "year": 2025, "free_lunch_eligible": 34.2, "english_learners": 12.1, "special_education": 8.4, "race_ethnicity": "Asian: 52%, White: 18%, Hispanic: 14%, Black: 2%"
| # | school_id | year | race_ethnicity | gender_split | free_lunch_eligible | english_learners |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews & Ratings objects from schooldigger.com. All fields typed and schema-versioned.
"school_id": "062271003230", "review_id": "REV-849201", "reviewer_type": "Parent", "star_rating": 4, "date_posted": "2025-10-14", "helpful_votes": 12, "sentiment_score": 0.82
| # | school_id | review_id | reviewer_type | star_rating | review_text | date_posted |
|---|---|---|---|---|---|---|
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Our SchoolDigger scraper navigates 50 distinct state assessment formats, paginates through decades of historical rankings, and extracts structured demographic data across every US public, private, and charter school.
NCES IDs, addresses, enrollment counts, student-to-teacher ratios, and magnet or charter designations scraped at the individual school level.
Capture year-over-year rank movement, state percentiles, and comparative scoring metrics across decades of available data.
Extract deeply nested state assessment tables, cross-tabulated by grade level, subject, and demographic subgroups.
Retrieve racial diversity indices, free lunch eligibility percentages, and special education enrollment stats per institution.
Roll up school data to the district level, capturing total expenditure per student, superintendent details, and district-wide rankings.
Extract boundary indicators and neighbourhood assignments to correlate housing data with local school quality.
Full review text, star ratings, reviewer type classification, and helpful vote counts paginated across all school profiles.
Run annual bulk exports post-assessment season or configure monthly pipelines to capture rolling review updates and enrollment shifts.
We map disparate state test formats (CAASPP, STAAR, NYSTP) into a unified, queryable schema for national comparison.
Brief in. Clean data out.
Provide state lists, district IDs, or school types. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and table parsing logic for schooldigger.com.
Schema validation, null-rate checks on test scores, and historical data sampling before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Education data is notoriously non-standard. Here is how we normalise 50 different state reporting structures and maintain pipeline resilience.
SchoolDigger employs rate limiting and bot detection to protect its proprietary ranking algorithms. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and request delays.
State assessment data is displayed in complex, multi-dimensional tables that change format based on the state and year. We deploy custom table-parsing algorithms that flatten these matrices into queryable relational rows.
Retrieving decades of school rankings requires navigating asynchronous pagination and hidden API endpoints. We trace the underlying network requests to extract historical datasets directly, bypassing UI limitations.
For national datasets, we maintain a hash index of last-seen values per school. Subsequent runs only push diffs when new assessment data or reviews are published, reducing storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in test scores, schema drift in district pages, and coverage drops.
Property portals integrate school rankings and district boundaries to enrich property listings and calculate neighbourhood value scores.
Sales teams map district budgets, student-teacher ratios, and technology expenditure to target enterprise software pitches.
Universities and think tanks analyse longitudinal test scores and demographic shifts to evaluate state-level policy effectiveness.
Corporate relocation firms use school quality matrices to recommend optimal neighbourhoods for transferring employees.
Tutoring franchises identify districts with dropping proficiency scores to deploy targeted local advertising campaigns.
Government agencies correlate demographic changes with charter school growth to model future infrastructure requirements.
"SchoolDigger aggregates the most comprehensive US education metrics, but extracting longitudinal test scores across 50 states requires serious pipeline engineering."
Most teams underestimate the complexity of state-by-state education data. Test score tables vary wildly, and district boundaries require spatial data extraction. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our schooldigger.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 dynamic table hydration for complex state assessment pages.
We maintain pools of US residential ISP proxies. Rotation happens per-request with sticky sessions where required to maintain stable connections during pagination.
Pipelines run on AWS Lambda and ECS. 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 schooldigger.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated school, district, and test score data. We do not circumvent authentication walls or extract proprietary API data without licenses. Clients should review SchoolDigger's ToS and consult legal counsel for specific use cases.
Every US state reports test scores differently. We build state-specific parsing modules that map local assessment metrics into a unified, normalised schema, allowing you to query CAASPP data alongside STAAR data seamlessly.
Yes. We paginate through historical data tabs to extract year-over-year ranking changes, enrollment shifts, and test score trends from the earliest available date on the platform.
School data is highly seasonal. We typically configure these pipelines to run monthly to capture new reviews, or annually in late summer to capture the latest state assessment results and enrollment figures.
We extract the boundary coordinate data and metadata associated with districts and schools when available in the DOM or underlying network requests, delivering it as structured geospatial arrays.
Our smallest packages start at a defined state list, typically 1 to 5 states. For national coverage across all 50 states, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off national school dump or continuous district monitoring across 50 states, we scope, build, and operate the pipeline. Tell us what you need.
