We extract public profiles, demographic distributions, education metrics, and partner preference signals from Matrimony domains. Delivered as clean JSON, CSV, or Parquet to your storage.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Public Profiles objects from matrimony.com. All fields typed and schema-versioned.
"profile_id": "M849201", "age": 28, "height": "5ft 8in", "religion": "Hindu", "caste": "Brahmin", "mother_tongue": "Hindi", "location": "Delhi, India", "marital_status": "Never Married"
| # | profile_id | age | height | religion | caste | sub_caste |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Education & Career objects from matrimony.com. All fields typed and schema-versioned.
"profile_id": "M849201", "education_level": "Masters", "degree": "MBA", "occupation": "Marketing Manager", "industry": "Corporate", "income_bracket": "INR 15 Lakhs to 20 Lakhs", "working_location": "Gurgaon"
| # | profile_id | education_level | degree | institution | occupation | industry |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Physical & Lifestyle objects from matrimony.com. All fields typed and schema-versioned.
"profile_id": "M849201", "diet": "Vegetarian", "smoking": "No", "drinking": "Occasionally", "body_type": "Athletic", "complexion": "Fair", "blood_group": "O+"
| # | profile_id | diet | smoking | drinking | body_type | complexion |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Family & Astrology objects from matrimony.com. All fields typed and schema-versioned.
"profile_id": "M849201", "family_type": "Nuclear", "family_status": "Upper Middle Class", "family_values": "Moderate", "star": "Rohini", "raasi": "Vrishabha", "dosham": "No"
| # | profile_id | family_type | family_status | family_values | father_occupation | mother_occupation |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Partner Preferences objects from matrimony.com. All fields typed and schema-versioned.
"profile_id": "M849201", "pref_age_min": 24, "pref_age_max": 28, "pref_height_min": "5ft 2in", "pref_marital_status": "Never Married", "pref_religion": "Hindu", "pref_education": "Bachelors or higher"
| # | profile_id | pref_age_min | pref_age_max | pref_height_min | pref_height_max | pref_marital_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Matrimony scraper handles the complex search grids, regional domain variations, and pagination structures required to build comprehensive demographic datasets.
Capture age, height, location, marital status, and basic demographic indicators from public search results and profile cards.
Extract degree information, professional industry, occupation categories, and stated income brackets.
Scrape religion, caste, sub-caste, mother tongue, star signs, and dosham indicators critical to matchmaking networks.
Extract the specific criteria users set for ideal partners, including age ranges, height requirements, and educational expectations.
Support for TamilMatrimony, TeluguMatrimony, KeralaMatrimony, and other regional portals under the parent network.
Navigate complex search filters and deep pagination to ensure high coverage of specific demographic segments.
Run recurring pipelines to track new profile creations, status changes, and demographic shifts over time.
Built-in proxy rotation and request throttling to handle rate limits and CAPTCHA challenges during extraction.
We standardise inconsistent text fields across different regional portals into a unified, query-ready format.
Brief in. Clean data out.
Provide target demographics, regional domains, or specific search parameters. We design the extraction schema.
We configure crawlers, proxy rotation, session management, and pagination handling for the target portals.
Schema validation, null-rate checks, and sample data review before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on schedule.
Matchmaking sites use aggressive rate limiting and complex regional domain structures. Here is how we maintain extraction reliability.
Matchmaking portals monitor IP request velocity strictly. Our crawlers use residential ISP proxies with randomised request timing to blend with normal user traffic and avoid IP bans.
The network operates dozens of regional sites with slight DOM variations. Our selector strategy abstracts these differences, delivering normalised data regardless of the source domain.
Search results often cap at a specific page limit. We dynamically slice search criteria by narrow age, height, and location bands to bypass display limits and ensure complete data capture.
Key profile details are often rendered via asynchronous JavaScript. We run Playwright browser sessions to ensure all dynamic content is fully hydrated before extraction.
User-entered fields like occupation or education vary wildly. We apply post-extraction cleaning rules to normalise these strings into structured, queryable categories.
Sociologists and researchers analyse marriage trends, caste preferences, and educational shifts across different regions.
Brands use demographic density data to target specific socio-economic segments for regional product launches.
New entrants in the dating and matchmaking space track user acquisition, active profiles, and regional dominance.
Machine learning teams use structured preference data to train recommendation algorithms and matching engines.
Analysts track changing partner preferences over time to identify macro shifts in societal values and expectations.
Researchers correlate stated income brackets and occupations with specific geographic and educational segments.
"Matrimony networks hold the most structured demographic and socio-economic preference data available, but extracting it requires navigating fragmented regional domains."
Most teams underestimate the complexity of scraping matchmaking portals. It requires handling heavy bot mitigation, regional domain variations, and complex search pagination. DataFlirt manages the infrastructure so your data science teams can focus on demographic analysis rather than proxy rotation.
Everything supported by our matrimony.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 manages JavaScript rendering and complex pagination structures.
We maintain ISP-grade residential proxies to distribute requests geographically, preventing IP blocks from aggressive rate limiting.
Pipelines run on AWS infrastructure. Airflow handles scheduling and dependency management, ensuring reliable delivery.
Data delivered to where your team already works — no new tooling required.
About matrimony.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible data is generally permissible. DataFlirt extracts only public, non-authenticated demographic and preference data. We do not extract private contact information, gated photos, or bypass authentication walls. Clients must ensure their specific use case complies with relevant privacy regulations.
No. Contact information on these platforms is gated behind premium paywalls and requires mutual consent. We strictly extract publicly visible demographic and preference signals.
Our pipelines are designed to recognise the underlying structural similarities across the network's regional domains. We map the data into a single, unified schema regardless of the source site.
Yes. By running scheduled pipelines against specific search criteria, we can identify new profile additions, status changes, and updates to user preferences.
Our minimum engagement typically starts with a defined demographic scope or specific regional domains delivered on a weekly schedule. Contact us to scope your requirements.
Yes. We offer sample extractions of up to 1,000 profiles based on your specific search criteria to validate data structure and completeness before deployment.
20-minute scoping call. Pilot dataset within the week. Production within two. Specify your target regions and demographic parameters. We handle the extraction infrastructure and deliver clean data to your warehouse.
