We extract course modules, instructor credentials, alumni placement stats, event schedules, and pricing from Scaler. 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 Details objects from scaler.com. All fields typed and schema-versioned.
"course_id": "SCL-DS-2026", "title": "Data Science & Machine Learning", "duration_months": 11, "skill_level": "Intermediate", "price_inr": 299000.0, "placement_assistance": true, "next_cohort_date": "2026-08-15"
| # | course_id | title | duration_months | skill_level | curriculum_summary | tech_stack |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Curriculum Modules objects from scaler.com. All fields typed and schema-versioned.
"module_id": "MOD-ML-01", "module_name": "Supervised Learning", "duration_weeks": 4, "topics_covered": "['Linear Regression', 'Logistic Regression', 'Decision Trees']", "tools_used": "['Python', 'Scikit-Learn']", "assessment_type": "Project Submission"
| # | module_id | course_id | module_name | duration_weeks | topics_covered | projects_included |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructor Profiles objects from scaler.com. All fields typed and schema-versioned.
"instructor_id": "INS-492", "name": "Anshuman Singh", "current_company": "Scaler", "past_companies": "['Facebook', 'Directi']", "role": "Co-founder", "courses_taught": "['System Design', 'Advanced DSA']"
| # | instructor_id | name | current_company | past_companies | role | bio |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Masterclasses & Events objects from scaler.com. All fields typed and schema-versioned.
"event_id": "EVT-8832", "title": "Cracking System Design Interviews", "date_time": "2026-06-10T18:00:00Z", "speaker_name": "Naman Bhalla", "speaker_company": "Google", "topic": "System Design"
| # | event_id | title | date_time | speaker_name | speaker_company | topic |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Alumni & Placements objects from scaler.com. All fields typed and schema-versioned.
"alumni_id": "ALU-10293", "previous_company": "Infosys", "current_company": "Amazon", "role": "SDE II", "salary_hike_pct": 120, "graduation_year": 2025
| # | alumni_id | name | previous_company | current_company | role | salary_hike_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Scaler scraper handles every layer of the platform: curriculum details, masterclass schedules, instructor credentials, and placement statistics - with JavaScript rendering and session management built in.
Title, duration, target audience, pricing, and EMI options scraped across all primary learning tracks.
Extract detailed module breakdowns, weekly topics, required tools, and project specifications.
Capture instructor names, current roles, past company affiliations, and courses taught.
Monitor upcoming masterclasses, speaker details, topics, and historical event archives.
Track course fees, scholarship details, and financing options available on the platform.
Extract aggregated placement statistics, top hiring companies, and average salary hikes.
Gather data on 1:1 mentorship structures, mentor profiles, and industry affiliations.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences.
Receive structured data in JSON, CSV, or Parquet, pushed directly to your warehouse.
Brief in. Clean data out.
Provide course URLs, event pages, or instructor lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for scaler.com.
Schema validation, null-rate checks, and data type verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Scaler relies heavily on dynamic rendering and gated components. Here is how we extract clean data reliably.
We use residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass basic scraping protections and rate limits on the platform.
Scaler uses modern front-end frameworks. We run full Playwright browser sessions with JavaScript execution to capture dynamically loaded curriculum modules and event schedules.
Our selector strategy uses multiple fallback chains per field, ensuring that minor UI updates to the course pages do not break your data pipeline.
For ongoing monitoring, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs. We alert on null-rate spikes or coverage drops and respond before you notice.
Competing platforms monitor course offerings, pricing changes, and instructor acquisitions to refine their own positioning.
Analysts track the introduction of new tech stacks and curriculum updates to gauge industry demand for specific skills.
Recruiters analyse alumni placement data and hiring company trends to source candidates from specific cohorts.
Universities and independent educators benchmark their syllabus against industry-leading programs.
EdTech companies track fee structures, EMI partnerships, and discount patterns to optimise their pricing models.
B2B service providers identify instructors and mentors for enterprise training partnerships.
"Scaler represents the benchmark for tech upskilling in India, but tracking their curriculum evolution and instructor network requires dedicated pipeline infrastructure."
Most teams underestimate the investment required: reliable Scaler scraping requires residential proxies, full JavaScript rendering, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our scaler.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 SPA interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required.
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 scaler.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Scaler is generally permissible. DataFlirt targets only public, non-authenticated course, instructor, and pricing data. We do not extract personal student data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass basic rate limiting.
We extract course titles, modules, pricing, EMI options, instructor profiles, masterclass schedules, and public alumni placement statistics.
Pipelines typically run on weekly or monthly cadences for course data. Masterclass schedules can be monitored daily.
Yes. We capture upcoming events, speaker details, topics, and registration links as they are published.
Absolutely. We provide a sample run covering a subset of courses or events during the pre-engagement scoping process.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off curriculum dump or a continuous event-monitoring feed - we scope, build, and operate the pipeline. Tell us what you need.
