We extract vendor profiles, venue pricing, real wedding metadata, and verified reviews from WedMeGood. 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 Venue Data objects from wedmegood.com. All fields typed and schema-versioned.
"venue_id": "V-10492", "name": "Taj West End", "city": "Bangalore", "locality": "Race Course Road", "venue_type": "Hotel, Banquet Hall", "cost_per_plate_veg": 2500, "cost_per_plate_nonveg": 3000, "capacity_max": 800, "rating": 4.8, "review_count": 142
| # | venue_id | name | city | locality | venue_type | cost_per_plate_veg |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Vendor Profiles objects from wedmegood.com. All fields typed and schema-versioned.
"vendor_id": "P-83912", "name": "The Wedding Story", "category": "Photographer", "city": "Mumbai", "base_price": 150000, "price_type": "per day", "rating": 4.9, "review_count": 312, "verified_badge": true, "years_experience": 8
| # | vendor_id | name | category | city | base_price | price_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from wedmegood.com. All fields typed and schema-versioned.
"review_id": "R-928173", "vendor_id": "P-83912", "user_name": "Aditi Sharma", "rating": 5, "review_text": "They captured our wedding perfectly. Highly recommend their candid photography.", "review_date": "2023-11-14", "event_type": "Wedding", "helpful_votes": 14
| # | review_id | vendor_id | user_name | rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Real Weddings objects from wedmegood.com. All fields typed and schema-versioned.
"wedding_id": "RW-4829", "title": "Pastel Themed Palace Wedding", "city": "Udaipur", "couple_names": "Rohan & Sneha", "wedding_date": "2023-12-05", "theme": "Royal, Pastel", "image_count": 45, "vendor_list": "['V-10492', 'P-83912']"
| # | wedding_id | title | city | couple_names | wedding_date | theme |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Bridal Wear objects from wedmegood.com. All fields typed and schema-versioned.
"product_id": "BW-9281", "vendor_id": "BWV-482", "title": "Crimson Red Zardosi Lehenga", "price": 85000, "outfit_type": "Lehenga", "material": "Raw Silk", "work_type": "Zardosi, Sequins", "delivery_time_days": 45, "customisation_available": true
| # | product_id | vendor_id | title | price | outfit_type | material |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our WedMeGood scraper navigates heavy JavaScript image grids, infinite scrolling, and regional vendor directories to extract structured pricing, reviews, and portfolio metadata.
Extract cost per plate (veg/non-veg), rental fees, minimum/maximum guest capacities, room counts, and available amenities for every venue.
Scrape photographer, makeup artist, and decorator profiles including base pricing, years of experience, and project counts.
Capture full review text, star ratings, event dates, helpful votes, and vendor responses across all categories.
Extract outfit types, pricing, materials, work types, and delivery timelines from designer and boutique listings.
Crawl vendor directories specific to Tier 1 and Tier 2 cities, capturing local market pricing and availability.
Map vendor relationships by extracting tagged vendors from real wedding showcases, including themes and colour palettes.
Extract high-resolution image URLs, alt text, and gallery categorisation without downloading heavy assets directly.
Track vendor placement and visibility scores within specific categories and cities.
Configure pipelines to track pricing changes and new reviews at daily, weekly, or monthly cadences.
Brief in. Clean data out.
Specify target cities, vendor categories, or specific URLs. We map the extraction schema to your requirements.
We configure Scrapy crawlers, handle infinite scrolling, and bypass rate limits using residential proxies.
Schema validation, null-rate checks, and price standardisation before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Scraping modern directory sites requires handling dynamic content and strict rate limits. Here is how our infrastructure manages the load.
WedMeGood relies heavily on infinite scrolling for vendor lists and lazy-loading for portfolio images. We execute full Playwright sessions to trigger scroll events and hydrate the DOM before extraction.
Directory scraping triggers aggressive IP bans. We route all requests through Indian residential ISP proxies, rotating IPs to maintain high concurrency without triggering Cloudflare blocks.
Vendor pricing formats vary wildly (e.g., 'per day', 'per function', 'starting from'). Our pipeline cleans and normalises these strings into queryable numeric fields and distinct price_type flags.
Front-end interfaces often cap search results at 50 pages. We bypass UI limitations by interacting directly with underlying API endpoints to extract the complete vendor catalogue for a given city.
Premium vendors have different profile layouts than standard listings. We use multi-layer XPath and CSS fallback chains to ensure data is extracted regardless of the profile tier.
Event planners and new vendors analyse category-specific pricing, cost per plate, and service packages across different cities to benchmark their own rates.
Alternative wedding platforms and directory services extract vendor profiles to enrich their own supplier databases and identify missing market segments.
Fashion retailers and decorators analyse real wedding metadata and colour palettes to forecast seasonal trends and popular themes.
SaaS companies selling to event professionals use vendor ratings, review counts, and portfolio sizes to score and qualify potential leads.
Hospitality groups extract venue reviews to run NLP sentiment analysis, identifying operational weaknesses and customer satisfaction drivers.
Established venues track new market entrants, promotional pricing, and review velocity to maintain their competitive edge.
"WedMeGood holds the most comprehensive index of Indian wedding vendors and pricing data — but it remains siloed until you build the extraction pipeline."
Most teams underestimate the investment required: reliable WedMeGood scraping requires residential proxies, handling heavy JavaScript image grids, managing pagination limits, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our wedmegood.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 infinite scrolling and lazy-loaded image grids.
We maintain pools of Indian residential ISP proxies to crawl regional directories without triggering Cloudflare blocks.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About wedmegood.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information is generally permissible. DataFlirt extracts only public vendor profiles, public reviews, and visible pricing. We do not extract private user data or bypass authentication walls. Clients should review WedMeGood's ToS and consult legal counsel for specific use cases.
No. Direct contact numbers and email addresses on WedMeGood are typically gated behind a lead generation form (Send Enquiry). We only extract data that is publicly visible on the vendor profile without requiring form submission.
We use Playwright to execute full browser sessions, programmatically triggering scroll events to load all vendors in a category before parsing the DOM. Where possible, we interact directly with the underlying pagination APIs.
Yes. We extract base prices, package costs, and specific metrics like veg/non-veg cost per plate for venues. Our pipeline normalises these varying text strings into clean numeric fields.
By default, we extract the high-resolution image URLs rather than downloading the binary files, which keeps delivery fast and storage costs low. If you require binary image delivery to S3, this can be configured as a custom pipeline.
We can configure pipelines to run daily, weekly, or monthly depending on your requirements. A full crawl of a major city directory typically completes within 4-6 hours.
Yes. We provide a sample run of up to 500 vendor profiles in a specific category and city to validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of venue pricing or a continuous feed of vendor reviews — we scope, build, and operate the pipeline. Tell us what you need.
