We extract course catalogues, university affiliations, syllabus structures, fee details, and alumni outcomes from Great Learning. 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 Information objects from greatlearning.com. All fields typed and schema-versioned.
"course_id": "GL-PG-DS-01", "title": "PG Program in Data Science and Business Analytics", "category": "Data Science", "university_partner": "University of Texas at Austin", "duration_months": 11, "format": "Online", "fee_inr": 250000, "rating": 4.6
| # | course_id | title | category | sub_category | university_partner | duration_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Syllabus & Modules objects from greatlearning.com. All fields typed and schema-versioned.
"course_id": "GL-PG-DS-01", "module_number": 3, "module_title": "Predictive Modeling", "topics_covered": "['Linear Regression', 'Logistic Regression', 'Decision Trees']", "duration_weeks": 4, "tools_covered": "['Python', 'Scikit-Learn']", "hands_on_projects": 2
| # | course_id | module_number | module_title | topics_covered | duration_weeks | hands_on_projects |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructor Profiles objects from greatlearning.com. All fields typed and schema-versioned.
"instructor_id": "INS-8492", "name": "Dr. Abhinanda Sarkar", "designation": "Academic Director", "company": "Great Learning", "courses_taught": "['Data Science', 'Machine Learning']", "academic_affiliation": "Stanford University", "linkedin_url": "https://linkedin.com/in/abhinanda-sarkar"
| # | instructor_id | name | designation | company | bio | courses_taught |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Outcomes objects from greatlearning.com. All fields typed and schema-versioned.
"review_id": "REV-99214", "course_id": "GL-PG-DS-01", "reviewer_name": "Rahul Sharma", "rating": 5, "current_role": "Data Analyst", "previous_role": "Software Engineer", "salary_hike_pct": 45, "placement_company": "Mu Sigma"
| # | review_id | course_id | reviewer_name | rating | review_text | current_role |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Cohorts objects from greatlearning.com. All fields typed and schema-versioned.
"course_id": "GL-PG-DS-01", "base_fee": 250000, "currency": "INR", "discount_pct": 0, "emi_options_available": true, "next_cohort_date": "2024-08-15", "application_deadline": "2024-08-01", "scholarship_available": true
| # | course_id | base_fee | currency | discount_pct | emi_options_available | next_cohort_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Great Learning scraper parses complex programme structures, university affiliations, and dynamic fee tables - bypassing rate limits and dynamic rendering to deliver clean curriculum data.
Category, sub-category, PG programmes, and free courses scraped at the individual course level with complete metadata.
Extract module-by-module breakdowns, project requirements, and tool coverage for deep curriculum analysis.
Capture partnership details with institutions like UT Austin, MIT IDSS, and Northwestern University.
Extract fee structures, currency variations based on IP, EMI options, and financing partner details.
Scrape hiring partner logos, reported salary hike percentages, and career transition statistics.
Capture industry experts and academic faculty profiles, including current designations and LinkedIn URLs.
Monitor application deadlines, batch start dates, and seat availability indicators.
Extract testimonials, star ratings, and detailed career transition narratives from past learners.
Track new course launches, fee adjustments, and updated syllabus modules on a daily or weekly cadence.
Brief in. Clean data out.
Provide category URLs, specific program domains, or instructor lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for greatlearning.com.
Schema validation, null-rate checks, and data normalisation across varied syllabus formats before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Great Learning uses modern SPA frameworks and dynamic routing. Here is how we extract structured curriculum data reliably.
Great Learning pages are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering to capture dynamic syllabus accordions and pricing widgets.
Instead of relying solely on brittle DOM selectors, our pipeline intercepts Next.js build props and internal API responses, extracting clean, structured JSON directly from the application state.
To prevent IP bans during full-catalogue crawls, we utilise residential ISP proxies. This ensures uninterrupted access and allows us to capture region-specific pricing accurately.
Different university partners display syllabi differently. Our extraction layer normalises these varied structures into a consistent, queryable format across all courses.
For ongoing pipelines, we maintain a hash index of last-seen values. Subsequent runs only push diffs, such as new cohort dates or fee changes, reducing downstream processing load.
EdTech platforms track course launches, fee structures, and university partnerships to position their own offerings.
Education portals and discovery platforms aggregate course data to build unified search experiences for learners.
Analysts identify trending skills, tools, and domain demands by tracking new module additions across top programmes.
Corporate training providers analyse curriculum gaps to pitch supplementary training to enterprises.
Researchers analyse EdTech pricing models, duration trends, and the impact of university branding on course fees.
Marketing teams identify high-demand course keywords and syllabus topics to inform their content creation pipelines.
"Great Learning holds a massive repository of modern curriculum data - but extracting structured syllabi across hundreds of university partners requires a dedicated pipeline."
Most teams underestimate the investment required: reliable EdTech scraping requires residential proxies, full JavaScript rendering for SPA frameworks, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our greatlearning.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 interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request to bypass rate limits and capture region-specific pricing without triggering blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About greatlearning.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Great Learning is generally permissible. DataFlirt targets only public course catalogues, syllabus outlines, and pricing data. We do not extract personal student data, circumvent authentication walls, or access proprietary video content.
Yes. We can filter and extract courses affiliated with specific institutions, such as UT Austin, MIT IDSS, or Northwestern University, capturing the exact branding and partnership details displayed.
We use Playwright to execute the JavaScript that populates these tables, allowing us to extract the base fee, discount percentages, and all listed EMI options accurately.
For continuous pipelines, we can configure weekly or daily runs to capture new course launches, updated cohort dates, and fee adjustments. Full catalogue refreshes typically complete within a few hours.
Yes. We extract the complete module-by-module breakdown, including module titles, topics covered, project requirements, and tools taught, normalising this data into a structured JSON array.
Yes. Every pipeline run produces timestamped snapshots. You can build a time-series table in your warehouse to track fee adjustments and discount patterns.
Absolutely. We provide a sample run of up to 50 courses as part of the pre-engagement scoping process, allowing you to validate the syllabus structure and field completeness.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous tracking of cohort dates and fee structures - we scope, build, and operate the pipeline. Tell us what you need.
