We extract vendor profiles, pricing packages, venue amenities, and review corpora from Shaadisaga. 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 Venues objects from shaadisaga.com. All fields typed and schema-versioned.
"vendor_id": "V-98234", "vendor_name": "The Taj Mahal Palace", "city": "Mumbai", "locality": "Colaba", "price_per_plate_veg": 3500.0, "price_per_plate_nonveg": 4000.0, "capacity_max": 800, "rating": 4.9, "reviews_count": 142
| # | vendor_id | vendor_name | city | locality | price_per_plate_veg | price_per_plate_nonveg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Makeup Artists objects from shaadisaga.com. All fields typed and schema-versioned.
"vendor_id": "MUA-4512", "vendor_name": "Namrata Soni", "city": "Mumbai", "price_bridal": 45000.0, "price_party": 15000.0, "travel_outstation": true, "trial_policy": "Paid Trial Available", "rating": 4.8, "reviews_count": 89
| # | vendor_id | vendor_name | city | price_bridal | price_party | price_engagement |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Photographers objects from shaadisaga.com. All fields typed and schema-versioned.
"vendor_id": "PH-7721", "vendor_name": "Stories by Joseph Radhik", "city": "Mumbai", "price_candid_per_day": 100000.0, "price_cinematography": 150000.0, "delivery_time_weeks": 8, "travel_costs": "Client bears travel and stay", "rating": 5.0, "reviews_count": 215
| # | vendor_id | vendor_name | city | price_candid_per_day | price_traditional_per_day | price_cinematography |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews objects from shaadisaga.com. All fields typed and schema-versioned.
"review_id": "REV-99123", "vendor_id": "V-98234", "author_name": "Priya Sharma", "rating": 5, "review_text": "The venue was spectacular and the catering exceeded expectations.", "review_date": "2025-11-12", "verified_booking": true, "helpful_votes": 12
| # | review_id | vendor_id | vendor_category | author_name | rating | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Real Weddings objects from shaadisaga.com. All fields typed and schema-versioned.
"article_id": "RW-4412", "title": "A Royal Jaipur Wedding With Pastel Hues", "couple_names": "Rohan & Aditi", "city": "Jaipur", "publish_date": "2025-10-05", "view_count": 15420, "vendor_tags": "['V-1123', 'PH-7721', 'MUA-4512']", "categories": "['Destination Wedding', 'Pastel Decor']"
| # | article_id | title | couple_names | city | publish_date | view_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Shaadisaga scraper handles every layer of the directory: nested vendor categories, dynamic pricing schemas, high-resolution portfolio metadata, and the full review corpus.
Full profile captures across 20+ categories including venues, photographers, decorators, and caterers.
Extract per-plate venue costs, candid photography rates, and bridal makeup packages into normalised columns.
Capture full review text, star ratings, event dates, verified booking flags, and vendor responses.
Scrape high-resolution image URLs, gallery counts, and category tags without downloading heavy binaries.
Extract precise localities, parking capacity, room counts, and specific venue policies.
Track vendor search position for specific categories across tier-1 and tier-2 cities.
Map featured real weddings back to the specific vendors who executed them.
Compile unified datasets for enterprise vendors operating in multiple categories simultaneously.
Monitor price changes and new vendor additions on weekly or monthly cadences.
Brief in. Clean data out.
Provide target cities, vendor categories, or specific URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for shaadisaga.com.
Schema validation, null-rate checks, and image URL verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Directory scraping requires navigating infinite scrolls and inconsistent pricing schemas. Here is how we build resilient pipelines.
Shaadisaga relies on heavy frontend frameworks for vendor lists and photo galleries. We use Playwright to simulate viewport scrolling, forcing DOM hydration to capture all paginated records.
Loading thousands of vendor portfolios slows down extraction. We intercept network requests to abort high-resolution image downloads while successfully capturing their source URLs for your dataset.
Venues charge per plate. Photographers charge per day. Makeup artists charge per event. We map these category-specific pricing models into clean, structured tables.
Some public contact details are masked behind 'View Phone Number' buttons. Our crawlers simulate these clicks and trigger the necessary API endpoints to extract the data.
Aggressive directory traversal triggers rate limits. We route requests through residential ISP proxies with realistic delays to maintain high throughput without blocks.
Wedding planners and aggregators benchmark venue and service pricing across cities to optimise their own offerings.
B2B suppliers extract vendor contact details to pitch wholesale decor, catering supplies, or management software.
New wedding tech platforms bootstrap their directories with baseline vendor data and amenity lists.
Analyse real wedding tags and portfolio images to identify rising decor, fashion, and destination trends.
Process vendor reviews to score reliability, punctuality, and quality of service at scale.
Event managers map out venue capacities, room availability, and catering rules for large-scale corporate events.
"The Indian wedding market operates on fragmented, opaque pricing. Shaadisaga holds the standardising data, provided you can extract it."
Extracting vendor data requires navigating infinite scrolls, inconsistent pricing schemas across categories, and heavy JavaScript rendering. DataFlirt handles the infrastructure, normalising complex vendor hierarchies into flat, queryable tables so your team can focus on analysis.
Everything supported by our shaadisaga.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 retry logic. Playwright forces DOM hydration for lazy-loaded vendor directories.
We maintain pools of residential ISP proxies. Rotation happens per-request to prevent rate limits during heavy directory traversal.
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 shaadisaga.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available vendor information is generally permissible under applicable law. DataFlirt targets only public directory data, pricing, and reviews. We do not extract personal user data or bypass authentication walls.
We use Playwright sessions to simulate browser scrolling, which forces the frontend framework to load and render all paginated vendor records.
Yes. We map category-specific pricing models, such as per-plate costs for venues and per-day rates for photographers, into normalised columns for easy querying.
Directory data is typically refreshed on weekly or monthly cadences depending on your requirements. Change detection ensures we only deliver updated records.
We extract the high-resolution source URLs for all portfolio images. Direct binary download requires a custom S3 pipeline, which we can provision upon request.
No. We do not submit fake leads or interact with the vendor contact forms. We only extract public or click-to-reveal contact details.
Our smallest packages start at a defined city or category list, typically encompassing 10,000 vendors, with monthly delivery.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off directory dump or continuous price monitoring across 20 cities, we scope, build, and operate the pipeline. Tell us what you need.
