We extract vendor profiles, venue capacities, pricing tiers, Real Wedding galleries, and bridal fashion trends from WeddingSutra. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Venue Data objects from weddingsutra.com. All fields typed and schema-versioned.
"venue_id": "WS-VEN-8472", "name": "Taj Lands End", "city": "Mumbai", "area": "Bandra West", "type": "5 Star Hotel", "capacity_max": 1200, "price_per_plate": 3500, "rating": 4.8
| # | venue_id | name | city | area | type | capacity_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Vendor Profiles objects from weddingsutra.com. All fields typed and schema-versioned.
"vendor_id": "WS-VND-3921", "name": "Stories by Joseph Radhik", "category": "Wedding Photographers", "city": "Mumbai", "starting_price": 500000, "rating": 4.9, "review_count": 142, "instagram_url": "https://instagram.com/storiesbyjosephradhik"
| # | vendor_id | name | category | city | starting_price | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Real Weddings objects from weddingsutra.com. All fields typed and schema-versioned.
"wedding_id": "WS-RW-9932", "couple_names": "Ananya & Rahul", "city": "Udaipur", "venue_name": "The Leela Palace", "photographer_name": "WeddingNama", "attire_designer": "Sabyasachi", "gallery_urls": "['url1.jpg', 'url2.jpg']"
| # | wedding_id | couple_names | city | date | venue_name | photographer_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Bridal Fashion objects from weddingsutra.com. All fields typed and schema-versioned.
"item_id": "WS-FSH-112", "designer": "Manish Malhotra", "category": "Lehenga", "collection_name": "Ruhaaniyat", "image_url": "https://img.weddingsutra.com/fsh/112.jpg", "price_estimate": "On Request", "related_vendors": "['WS-VND-883']"
| # | item_id | designer | category | collection_name | image_url | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Vendor Reviews objects from weddingsutra.com. All fields typed and schema-versioned.
"review_id": "REV-99231", "vendor_id": "WS-VND-3921", "user_name": "Priya S.", "rating": 5, "date": "2023-11-14", "review_text": "Absolutely stunning photography for our sangeet.", "event_type": "Sangeet", "helpful_votes": 12
| # | review_id | vendor_id | user_name | rating | date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our WeddingSutra scraper handles every layer of the platform: vendor directories, complex venue pricing, real wedding visual metadata, and review aggregation - with JavaScript rendering built in.
Extract comprehensive profiles for photographers, makeup artists, planners, and decorators across all Indian cities.
Capture granular venue details including per-plate pricing, minimum/maximum guest capacities, and available spaces.
Map couples to their chosen venues, photographers, and designers through structured tags on Real Wedding posts.
Scrape designer collections, apparel categories, and trending bridal wear imagery with associated metadata.
Extract full review text, star ratings, and event types to gauge vendor reputation and client satisfaction.
Navigate infinite-scroll galleries to extract high-resolution image URLs for computer vision or mood board generation.
Maintain the exact category and geographical hierarchy used by WeddingSutra to organise your competitive intelligence.
Extract public social media links and business contact information where available on vendor profiles.
Run continuous pipelines at weekly or monthly cadences to detect new vendors and updated pricing tiers.
Brief in. Clean data out.
Provide target cities, vendor categories, or specific Real Wedding sections. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and pagination logic for weddingsutra.com.
Schema validation, null-rate checks, and data normalisation testing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from visual-heavy directories requires specific infrastructure. Here is how we maintain pipeline stability.
WeddingSutra heavily relies on JavaScript for lazy-loading images and rendering dynamic vendor portfolios. We run full Playwright browser sessions to ensure all visual assets and contact details are fully hydrated before extraction.
Real Wedding galleries and vendor lists often use infinite scroll rather than traditional pagination. Our crawlers programmatically trigger these load events, capturing the complete dataset without timing out.
Vendor profiles vary wildly depending on their subscription tier on the platform. Our selector strategy uses fallback chains to gracefully handle missing fields, ensuring structured output even from unstructured profile text.
For tracking pricing updates or new reviews, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing downstream processing load and storage costs.
To prevent IP bans during high-volume directory scrapes, our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing.
New wedding platforms seed their directories by extracting structured vendor profiles, categories, and geographical coverage.
Hospitality groups monitor competitor venue pricing, per-plate costs, and capacity tiers across different cities.
Apparel brands analyse Real Wedding metadata to identify trending designers, colours, and styles in specific regions.
B2B suppliers extract vendor contact information and social handles to build targeted outreach lists for wholesale products.
Fintech and planning apps use aggregated pricing data to build accurate budget calculators for prospective couples.
Agencies scrape vendor reviews to perform sentiment analysis, identifying the highest-rated service providers in niche categories.
"WeddingSutra holds the most comprehensive taxonomy of the Indian wedding industry - extracting it requires navigating infinite scrolls and unstructured vendor tags."
Extracting data from visual-heavy wedding directories introduces unique challenges: heavy DOM structures, lazy-loaded image grids, and inconsistent vendor metadata formatting. DataFlirt handles the JavaScript rendering and pagination logic, delivering clean, normalised datasets so your team can focus on market analysis instead of scraper maintenance.
Everything supported by our weddingsutra.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, infinite scrolling, and visual element hydration. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies to avoid rate limits when scraping large vendor directories. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About weddingsutra.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from WeddingSutra is generally permissible under applicable law for non-authenticated data. DataFlirt targets only public vendor profiles, pricing, and reviews. We do not extract personal user data or circumvent authentication walls. Clients should review platform terms of service and consult legal counsel for specific use cases.
Yes. We navigate the image galleries and extract the source URLs for high-resolution images, rather than the compressed thumbnails, allowing you to build computer vision datasets or mood boards.
Where contact numbers are masked behind JavaScript events, our Playwright integration programmatically interacts with the DOM to reveal and extract the complete contact information.
Yes. We can configure the pipeline to target specific geographical nodes in the WeddingSutra taxonomy, isolating data to your exact market requirements.
Yes. We capture starting prices for photographers and makeup artists, as well as complex pricing tiers for venues including per-plate costs for vegetarian and non-vegetarian options.
We can schedule pipeline runs at daily, weekly, or monthly intervals depending on your needs. Change-detection ensures you only process updated profiles or new reviews.
Yes. When a Real Wedding post tags a specific vendor, we extract that relationship, allowing you to map portfolio items back to the vendor's main directory profile.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete dump of Indian wedding venues or continuous tracking of bridal fashion trends - we scope, build, and operate the pipeline. Tell us what you need.
