We extract public profiles, community demographics, education backgrounds, and profession signals from Shaadi.com. 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 Basic Profile objects from shaadi.com. All fields typed and schema-versioned.
"profile_id": "SH12345678", "age": 28, "height": "5'6"", "gender": "Female", "religion": "Hindu", "mother_tongue": "Hindi", "location": "Mumbai, Maharashtra"
| # | profile_id | age | height | gender | marital_status | religion |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Education & Career objects from shaadi.com. All fields typed and schema-versioned.
"profile_id": "SH12345678", "highest_education": "MBA", "occupation": "Marketing Professional", "income_range": "INR 15 Lakh to 25 Lakh", "employed_in": "Private Sector", "working_location": "Mumbai"
| # | profile_id | highest_education | college_name | employed_in | occupation | income_range |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Family Background objects from shaadi.com. All fields typed and schema-versioned.
"profile_id": "SH12345678", "family_status": "Middle Class", "family_type": "Nuclear", "family_values": "Moderate", "brothers_count": 1, "living_with_parents": true
| # | profile_id | family_status | family_type | family_values | father_occupation | mother_occupation |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lifestyle & Astrology objects from shaadi.com. All fields typed and schema-versioned.
"profile_id": "SH12345678", "diet": "Vegetarian", "smoke_status": "No", "manglik": "No", "rashi_moon_sign": "Leo", "gotra": "Kashyap", "star": "Magha"
| # | profile_id | diet | smoke_status | drink_status | blood_group | rashi_moon_sign |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Partner Preferences objects from shaadi.com. All fields typed and schema-versioned.
"profile_id": "SH12345678", "pref_age_min": 28, "pref_age_max": 32, "pref_religion": "Hindu", "pref_education": "Masters", "pref_income": "INR 20 Lakh and above", "pref_marital_status": "Never Married"
| # | profile_id | pref_age_min | pref_age_max | pref_height_min | pref_height_max | pref_marital_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Shaadi.com scraper handles the complexity of matrimonial data extraction: dynamic profile loading, infinite scroll, and aggressive rate limiting. We deliver structured demographic datasets ready for analysis.
Extract basic stats, location, height, age, and marital status from public facing profile cards.
Capture degrees, universities, occupations, and self-reported income brackets.
Parse detailed community data including religion, caste, subcaste, and mother tongue.
Extract Manglik status, Nakshatra, Rashi, and Gotra for cultural compatibility matching.
Track dietary preferences, smoking habits, and drinking status.
Capture family type, traditional values, and sibling counts.
Extract the desired criteria for matches, including age gaps and income expectations.
Identify VIP or premium membership badges on active profiles.
Track current working location versus native place and citizenship status.
Run one-off bulk exports or configure continuous pipelines with change detection.
Brief in. Clean data out.
Provide community filters, location targets, or age brackets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for shaadi.com.
Schema validation, null-rate checks, and sample profile data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Matrimonial sites deploy strict rate limits and complex DOM structures. Here is how we maintain reliable extraction.
Shaadi.com tracks request velocity strictly. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to simulate normal user behaviour.
Profile lists and detailed views rely on heavy JavaScript execution and infinite scroll. We run full Playwright browser sessions to trigger lazy loading and hydrate all profile fields.
Users leave many fields blank, causing layout shifts. Our selectors use robust fallback chains to ensure missing data does not break the parsing logic.
We maintain a hash index of last-seen values per profile. Subsequent runs only push diffs, reducing downstream processing load.
We monitor for redirect loops and CAPTCHA threshold breaches in real time, automatically rotating proxy pools before data quality degrades.
Sociologists and researchers analyze marriage trends, age distributions, and community clustering across regions.
Planners estimate target audience size for wedding services, venues, and related industries.
Machine learning teams train recommendation engines and matching algorithms on real preference data.
Analysts track geographic mobility and inter-community marriage preferences over time.
Map self-reported income brackets against education levels and geographic locations.
Build propensity models for high-value users based on lifestyle indicators and premium tags.
"Shaadi.com holds the largest structured dataset of Indian demographic, educational, and cultural preferences available anywhere on the public web."
Extracting matrimonial data requires navigating strict rate limits, dynamic JavaScript payloads, and aggressive bot mitigation. DataFlirt manages the proxy rotation, session persistence, and parsing logic so your team can focus on demographic analysis and model training instead of maintaining scrapers.
Everything supported by our shaadi.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 and 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 shaadi.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated profile data. We do not extract personal data behind login walls or violate user privacy.
We use residential ISP proxies and request timing modelled on human behaviour to avoid triggering security systems.
No. We do not bypass authentication to extract private phone numbers or email addresses.
We can target any public search parameter including religion, caste, mother tongue, and location.
Pipelines can be configured for daily or weekly runs depending on your requirements and volume.
We extract public image URLs, but we cannot extract photos set to private or restricted to accepted connections.
Our smallest packages start at 50,000 profiles per run. Contact us with your specific demographic targets for a quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off community extract or continuous tracking of matrimonial trends across millions of profiles. Tell us what you need.
