We extract college directories, fee structures, placement records, exam cut-offs, and student reviews from Shiksha. 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 shiksha.com. All fields typed and schema-versioned.
"college_id": "COL-8492", "name": "RV College of Engineering", "location": "Bengaluru", "state": "Karnataka", "university_affiliation": "Visvesvaraya Technological University", "establishment_year": 1963, "ownership_type": "Private", "total_faculty": 380
| # | college_id | name | location | state | university_affiliation | establishment_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Courses & Fees objects from shiksha.com. All fields typed and schema-versioned.
"course_id": "CRS-91823", "college_id": "COL-8492", "course_name": "B.E. in Computer Science and Engineering", "degree_type": "UG", "duration_months": 48, "total_tuition_fee": 386000, "fee_currency": "INR", "seats_available": 180
| # | course_id | college_id | course_name | degree_type | duration_months | total_tuition_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Placement Records objects from shiksha.com. All fields typed and schema-versioned.
"college_id": "COL-8492", "placement_year": 2023, "highest_salary": 6200000, "average_salary": 1450000, "currency": "INR", "placement_percentage": 94.5, "top_recruiters": "['Microsoft', 'Amazon', 'Cisco', 'Goldman Sachs']"
| # | college_id | placement_year | highest_salary | average_salary | median_salary | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Student Reviews objects from shiksha.com. All fields typed and schema-versioned.
"review_id": "REV-551928", "college_id": "COL-8492", "course_name": "B.E. in Computer Science", "reviewer_batch": "2024", "overall_rating": 4.2, "placement_rating": 4.8, "infrastructure_rating": 3.5, "review_date": "2023-11-14"
| # | review_id | college_id | course_name | reviewer_batch | overall_rating | placement_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Exam Cut-Offs objects from shiksha.com. All fields typed and schema-versioned.
"exam_name": "KCET", "college_id": "COL-8492", "course_name": "B.E. in Computer Science", "category": "General", "round_number": 2, "opening_rank": 142, "closing_rank": 315, "year": 2023
| # | exam_name | college_id | course_name | category | quota | round_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Shiksha scraper navigates complex categorisation, nested fee tables, and aggressive lead-generation overlays to extract clean, structured datasets across engineering, medical, and management domains.
Extract institution name, location, university affiliation, establishment year, campus size, and infrastructure details.
Capture tuition fees, duration, seat availability, eligibility criteria, and accepted entrance exams for every course variant.
Extract highest, average, and median CTCs alongside top recruiter lists and overall placement percentages.
Paginate through student reviews capturing granular ratings for faculty, infrastructure, placements, and campus life.
Extract opening and closing ranks across multiple counselling rounds, categories, and quotas for major entrance exams.
Track application start dates, deadlines, and exam schedules published across college pages.
Extract total faculty counts, library specifications, hostel availability, and laboratory facilities.
Circumvent mandatory login walls and brochure download popups to access underlying page data.
Configure monthly or quarterly pipelines to track fee revisions and new admission cycle cut-offs.
Brief in. Clean data out.
Provide target domains, categories (e.g., MBA, B.Tech), or specific college lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and popup handling for shiksha.com.
Schema validation, null-rate checks, and fee outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Education portals use aggressive lead-capture mechanisms and complex DOM structures. Here is how we maintain data flow.
Shiksha frequently obscures content with mandatory lead-generation forms and brochure download modals. We use Playwright to intercept these DOM events, dismiss overlays, and extract the underlying HTML without triggering lead submissions.
Critical data like exam cut-offs and historical fee trends load via asynchronous API calls after the initial page render. Our crawlers execute full JavaScript sessions to ensure these tables are fully hydrated before extraction.
Engineering college profiles have entirely different DOM structures compared to medical or design institutes. Our extraction pipelines use domain-specific selector chains to ensure high fill rates regardless of the academic category.
Student reviews and Q&A sections rely on infinite scroll mechanics. We simulate human scrolling behaviour to trigger subsequent API loads, ensuring the entire review corpus is captured.
Aggressive scraping triggers immediate IP bans. We distribute requests across a pool of Indian residential proxies, maintaining low concurrency per IP to mimic organic student browsing patterns.
Analyse fee structures, course availability, and seat capacities across regions to identify market gaps for new program launches.
Universities track rival placement statistics, tuition fee adjustments, and infrastructure investments to benchmark their own offerings.
Train machine learning models on historical exam cut-offs and opening/closing ranks to predict admission probabilities for students.
Run NLP pipelines over thousands of student reviews to extract qualitative insights regarding faculty competence and campus life.
Populate downstream educational portals, counselling apps, and student forums with structured, up-to-date college directories.
Education loan providers use fee structure data to validate loan requests and assess the financial requirements for specific courses.
"Shiksha holds the most comprehensive map of Indian higher education — but accessing fee structures and cut-offs at scale requires bypassing layers of lead-gen modals."
Extracting data from Shiksha involves navigating aggressive lead-capture popups, heavily nested JavaScript tables, and inconsistent schema layouts between engineering and medical colleges. DataFlirt handles the DOM interactions and proxy rotation, delivering clean, normalised datasets to your warehouse.
Everything supported by our shiksha.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, 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 across IN/US/UK/DE regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 shiksha.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information, fee structures, and reviews is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal user data or bypass authenticated login walls. Clients should review Terms of Service and consult legal counsel for specific use cases.
Shiksha uses aggressive lead-generation modals that obscure page content. We use Playwright to programmatically dismiss these DOM elements or intercept the network requests, allowing us to extract the underlying data without submitting false leads.
Yes. Where Shiksha surfaces historical cut-off data across multiple years, counselling rounds, and social categories, we extract and structure it into a normalised time-series format.
Education data is less volatile than eCommerce pricing. Most clients run weekly or monthly pipelines to capture new reviews, fee updates, and admission cycles. Full catalogue refreshes typically complete within 12-24 hours.
No. We extract structured placement statistics (highest CTC, average CTC, top recruiters) that are rendered in HTML tables. We do not perform OCR on raw PDF placement reports uploaded by colleges.
We extract the raw course nomenclature as displayed on the target page (e.g., 'B.E. in CSE' vs 'B.Tech Computer Science'). Standardisation and mapping to canonical course IDs should be handled downstream in your warehouse.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off college directory dump or a continuous feed of exam cut-offs and student reviews — we scope, build, and operate the pipeline. Tell us what you need.
