We extract college directories, programme fees, entrance cut-offs, placement stats, and student reviews from CollegeDekho. 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 collegedekho.com. All fields typed and schema-versioned.
"college_id": "CD-8492", "college_name": "RV College of Engineering", "location_city": "Bangalore", "location_state": "Karnataka", "established_year": 1963, "ownership_type": "Private", "affiliation": "Visvesvaraya Technological University", "approvals": "['AICTE', 'NBA']"
| # | college_id | college_name | location_city | location_state | established_year | ownership_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Courses & Fees objects from collegedekho.com. All fields typed and schema-versioned.
"college_id": "CD-8492", "course_name": "B.E. in Computer Science and Engineering", "degree_level": "UG", "stream": "Engineering", "duration_years": 4, "total_fees_inr": 386000, "first_year_fees_inr": 96500, "accepted_exams": "['KCET', 'COMEDK UGET']"
| # | college_id | course_name | degree_level | stream | duration_years | study_mode |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Cut-offs & Exams objects from collegedekho.com. All fields typed and schema-versioned.
"college_id": "CD-8492", "course_name": "B.E. in Computer Science and Engineering", "exam_name": "KCET", "exam_year": 2024, "counselling_round": "Round 1", "category": "General", "opening_rank": 45, "closing_rank": 158
| # | college_id | course_name | exam_name | exam_year | counselling_round | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Placements objects from collegedekho.com. All fields typed and schema-versioned.
"college_id": "CD-8492", "placement_year": 2023, "highest_package_inr": 6200000, "average_package_inr": 1450000, "placement_percentage": 94.5, "total_recruiters": 284, "top_recruiters": "['Microsoft', 'Amazon', 'Cisco', 'Goldman Sachs']"
| # | college_id | placement_year | highest_package_inr | average_package_inr | median_package_inr | placement_percentage |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Student Reviews objects from collegedekho.com. All fields typed and schema-versioned.
"review_id": "REV-928411", "college_id": "CD-8492", "course_enrolled": "B.E. in Computer Science", "overall_rating": 4.5, "placement_rating": 4.8, "faculty_rating": 4.2, "review_title": "Excellent placements but strict academics", "review_date": "2025-08-14"
| # | review_id | college_id | reviewer_name | course_enrolled | graduation_year | overall_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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CollegeDekho structures a highly fragmented sector. Our pipeline extracts every nested table, dynamic filter, and paginated review corpus — normalising the data for immediate downstream analysis.
Extract affiliations, approvals (AICTE, UGC), campus size, faculty counts, and facility lists across 43,000+ institutions.
Map every course variant, duration, study mode, and detailed fee breakdown (first year vs total) across all streams.
Capture opening and closing ranks across multiple exams (JEE, NEET, CAT, KCET), categorised by quota and counselling round.
Extract highest, average, and median salary packages, along with recruiter lists and placement percentages per academic year.
Paginate through thousands of student reviews, capturing granular ratings for placements, infrastructure, and faculty.
Track application start dates, exam schedules, and counselling deadlines for upcoming academic sessions.
Extract hostel availability, gender-specific capacity, and annual fee structures where listed.
Our Playwright infrastructure navigates complex state/city/stream filters to ensure zero data omission across the directory.
Configure high-frequency pipelines during peak admission seasons to capture real-time cut-off and seat availability updates.
Brief in. Clean data out.
Specify target streams (e.g., Engineering, Medical), states, exams, or specific college parameters. We design the schema.
We configure Scrapy and Playwright to handle CollegeDekho's dynamic DOM, accordions, and API endpoints.
Schema validation, null-rate checks on critical fields like fees and cut-offs, and sample deliveries.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on your required cadence.
CollegeDekho relies heavily on client-side rendering and nested accordions to display dense educational data. Here is how we extract it reliably.
Fee structures and cut-off ranks are often hidden behind JavaScript accordions and dynamic tabs. We use Playwright to simulate user interactions, expanding all nodes before parsing the DOM to ensure complete data extraction.
Where CollegeDekho uses infinite scroll for reviews and college lists, our pipeline intercepts the underlying XHR/Fetch requests. This bypasses the DOM entirely, extracting clean JSON payloads for higher throughput and reliability.
College descriptions and eligibility criteria are often free-text blocks. We apply post-processing regex and NLP to extract structured variables (e.g., extracting '50% in 10+2' into a clean integer field) before delivery.
To prevent IP bans during high-volume directory crawls, we route requests through Indian residential proxy pools, rotating IPs per request and matching browser TLS fingerprints to appear as legitimate organic traffic.
During admission seasons, data changes daily. We maintain a hash index of last-seen values per college. Subsequent runs only push diffs, alerting you to updated fee structures or revised cut-off lists.
Competitor platforms use directory data to backfill their own databases, ensuring parity in college coverage and fee accuracy.
Admissions consultants query historical cut-off data and placement records to build predictive admission models for students.
Banks and NBFCs use fee structures and placement metrics to underwrite education loans and determine college tier classifications.
Analysts track the proliferation of specific courses (e.g., AI/ML specialisations) and fee inflation trends across private universities.
Institutions monitor competitor fee structures, facility offerings, and student review sentiment to optimise their own positioning.
Service providers targeting educational institutions extract college contact details, affiliations, and faculty counts to build targeted outreach lists.
"CollegeDekho aggregates the most fragmented sector in India — higher education — but standardising that data for downstream analysis requires dedicated infrastructure."
Most teams underestimate the complexity of extracting Indian education data: inconsistent fee tables, nested cut-off accordions, and dynamic filter states. DataFlirt manages the residential proxies, JavaScript rendering, and schema normalisation so your engineers can focus on product development and analysis.
Everything supported by our collegedekho.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 manages crawl logic and deduplication, while Playwright handles the client-side rendering required for CollegeDekho's dynamic components.
We utilise Indian residential proxies to mimic legitimate domestic traffic, preventing blockades during large-scale directory crawls.
Pipelines are orchestrated via Apache Airflow on Kubernetes, ensuring reliable scheduling and automated delivery to your data warehouse.
Data delivered to where your team already works — no new tooling required.
About collegedekho.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information, fee structures, and reviews is generally permissible. DataFlirt extracts only public, non-authenticated data. We do not bypass OTP walls, extract student PII, or access internal application dashboards.
We use Playwright to execute JavaScript, simulate clicks on accordions, and select different exam/category tabs before parsing the DOM. This ensures we capture all permutations of cut-offs and fees.
Yes. We maintain historical snapshots. By running pipelines at regular intervals (e.g., monthly or quarterly), we calculate diffs and flag fee increases or new course additions.
We can configure high-frequency pipelines during peak periods (May to August) to scrape cut-off updates and seat availability daily.
Engagements typically start with a defined subset (e.g., all Engineering colleges in South India) or a full directory baseline extract. Contact us to scope your specific volume requirements.
Yes. We provide a sample extract of 100-200 colleges, including nested course and placement data, during the scoping phase to validate schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. From targeted state-level college lists to a full national directory extraction — we build and operate the pipeline. Tell us your requirements.
