We extract course catalogues, pricing tiers, module structures, and faculty profiles from Byjus. 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 Course Listings objects from byjus.com. All fields typed and schema-versioned.
"course_id": "BYJ-K12-MATH-09", "title": "Class 9 Mathematics Complete Course", "category": "K-12", "target_audience": "Class 9 Students", "language": "English", "price": 25000.0, "duration": "12 Months", "module_count": 15
| # | course_id | title | category | target_audience | language | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus Structure objects from byjus.com. All fields typed and schema-versioned.
"course_id": "BYJ-K12-MATH-09", "module_id": "MOD-ALG-01", "module_name": "Algebraic Expressions", "duration_minutes": 120, "resource_count": 5, "difficulty_level": "Intermediate", "video_count": 3
| # | course_id | module_id | module_name | topic_list | duration_minutes | resource_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing and Offers objects from byjus.com. All fields typed and schema-versioned.
"course_id": "BYJ-K12-MATH-09", "base_price": 30000.0, "discounted_price": 25000.0, "discount_pct": 16.6, "emi_available": true, "emi_starting_price": 2500.0, "subscription_duration": "12 Months"
| # | course_id | base_price | discounted_price | discount_pct | emi_available | emi_starting_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Faculty Profiles objects from byjus.com. All fields typed and schema-versioned.
"faculty_id": "FAC-MATH-882", "name": "Rahul Sharma", "subject": "Mathematics", "qualifications": "M.Sc Mathematics", "years_experience": 8, "courses_taught": 12, "rating": 4.8
| # | faculty_id | name | subject | qualifications | years_experience | courses_taught |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Exam Prep Data objects from byjus.com. All fields typed and schema-versioned.
"exam_name": "JEE Main", "year": 2026, "previous_papers_count": 15, "mock_tests_count": 40, "success_rate_claim": "Top 100 All India Rankers", "total_questions": 1500, "syllabus_coverage": "100%"
| # | exam_name | year | previous_papers_count | mock_tests_count | success_rate_claim | total_questions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Byjus scraper handles every layer of the platform: course listings, dynamic pricing, syllabus mapping, and faculty intelligence, with JavaScript rendering and anti-bot circumvention built in.
Title, category, language, duration, and target audience scraped at the course level.
Extract hierarchical module structures, topic lists, and video lesson metadata across all subjects.
Capture base prices, discounts, EMI options, and subscription tiers across different regions.
Extract tutor names, qualifications, experience metrics, and student ratings.
Track mock test availability, previous year paper counts, and syllabus coverage for JEE, NEET, and IAS.
Scrape course metadata across Hindi, Marathi, Bengali, and other regional language offerings.
Extract document titles, PDF availability flags, and revision note summaries.
Map offline centre data, hybrid course offerings, and integrated classroom pricing.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences.
Brief in. Clean data out.
Provide category URLs, exam types, or target demographics. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for byjus.com.
Schema validation, null-rate checks, and hierarchy mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
EdTech platforms rely on complex SPA architectures and API-driven content. Here is how we extract clean data from messy frontends.
Byjus uses heavy client-side rendering. We run full Playwright browser sessions to execute JavaScript and hydrate course pages before extraction.
Rather than parsing messy DOM trees, our pipeline intercepts Next.js data props and backend API responses to extract clean JSON payloads directly from the network tab.
Course structures are deeply nested. Our schema normalises modules, chapters, and topics into a flat, relational format suitable for SQL databases.
Course prices change based on IP location. We use state-specific residential proxies in India to capture accurate regional pricing and EMI offers.
For large course catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
Rival platforms monitor course offerings, pricing tiers, and new subject launches to maintain competitive parity.
Educational content creators map Byjus syllabus structures to identify content gaps in their own platforms.
Strategy teams track discount frequencies, EMI structures, and regional price variations to optimise their own revenue models.
Analysts track the expansion of regional language courses and competitive exam prep categories to gauge market demand.
Machine learning teams use structured syllabus and topic taxonomies to train educational large language models.
Researchers analyse the evolution of digital pedagogy and curriculum design across different K-12 segments.
"Byjus contains one of the most comprehensive digital curricula in the world, but mapping that taxonomy requires a resilient extraction pipeline."
Most teams underestimate the investment required: reliable Byjus scraping requires full JavaScript rendering, handling complex Next.js state objects, API interception, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our byjus.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 Next.js hydration.
We maintain pools of residential ISP proxies across Indian states to capture accurate regional pricing and circumvent bot detection.
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 byjus.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Byjus is generally permissible under applicable law. DataFlirt targets only public, non-authenticated course catalogues, pricing, and syllabus data. We do not extract personal student data or circumvent authentication walls.
Byjus relies heavily on Next.js and client-side rendering. We use full Playwright browser sessions and intercept backend API calls to extract clean data payloads directly, bypassing messy DOM parsing.
No. We extract video metadata available on public course pages, but we do not bypass paywalls to download proprietary video content.
Full catalogue refreshes at daily or weekly cadences complete within a 6 to 12 hour window depending on size, ensuring you capture the latest discount campaigns and EMI changes.
Yes. Our schema captures the full hierarchy of grades, subjects, modules, chapters, and individual topics, outputting a clean relational dataset.
Absolutely. We provide a sample run of up to 100 courses as part of the pre-engagement scoping process, so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off syllabus dump or continuous pricing intelligence across the entire catalogue, we scope, build, and operate the pipeline. Tell us what you need.
