We extract venue listings, plate pricing, capacity limits, vendor portfolios, and reviews from Venuelook. 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 Listings objects from venuelook.com. All fields typed and schema-versioned.
"venue_id": "VL-84920", "name": "The Leela Ambience", "type": "5 Star Hotel", "city": "Delhi", "locality": "Gurugram", "rating": 4.8, "review_count": 342, "event_types": "['Wedding', 'Corporate', 'Reception']"
| # | venue_id | name | type | city | locality | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Capacity objects from venuelook.com. All fields typed and schema-versioned.
"venue_id": "VL-84920", "veg_plate_price": 2500.0, "non_veg_plate_price": 2800.0, "min_capacity": 100, "max_capacity": 1500, "parking_capacity": 500, "rooms_available": 322
| # | venue_id | veg_plate_price | non_veg_plate_price | rental_price | min_capacity | max_capacity |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Vendor Profiles objects from venuelook.com. All fields typed and schema-versioned.
"vendor_id": "VND-9182", "name": "Rohan Photography", "category": "Photographer", "city": "Mumbai", "base_price": 50000.0, "rating": 4.9, "review_count": 128, "experience_years": 7
| # | vendor_id | name | category | city | base_price | pricing_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from venuelook.com. All fields typed and schema-versioned.
"review_id": "REV-44829", "entity_id": "VL-84920", "entity_type": "Venue", "reviewer_name": "Amit S.", "rating": 5.0, "review_date": "2023-11-12", "review_text": "Excellent hospitality and food.", "verified_booking": true
| # | review_id | entity_id | entity_type | reviewer_name | rating | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search Results objects from venuelook.com. All fields typed and schema-versioned.
"keyword": "banquet hall", "city": "Bangalore", "position": 3, "entity_id": "VL-11234", "name": "Royal Palace Banquet", "base_price": 800.0, "rating": 4.2, "scraped_at": "2023-11-15T08:12:00Z"
| # | keyword | city | locality | position | entity_id | name |
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Our Venuelook scraper handles the platform's infinite scroll, dynamic pricing widgets, and unstructured amenity lists — delivering clean, normalised datasets ready for your warehouse.
Capture name, exact address, geo-coordinates, venue types, and establishment years across all listed properties.
Extract vegetarian and non-vegetarian per-plate costs, hall rental fees, and tax inclusions for every venue.
Normalise floating versus seating capacities, indoor/outdoor splits, and minimum guest requirements.
Extract profiles for photographers, makeup artists, decorators, and caterers including base pricing and service lists.
Structure unstructured policy data: alcohol rules, DJ permissions, parking limits, and accommodation availability.
Collect full review text, star ratings, event context, and management responses across venues and vendors.
Track visibility and ranking for specific localities and venue types across different Indian cities.
Scale extraction across Delhi NCR, Mumbai, Bangalore, and Tier 2 cities using location-specific parameters.
Track pricing changes and new listings during peak wedding seasons with automated daily or weekly runs.
Brief in. Clean data out.
Provide target cities, venue types, or vendor categories. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and pagination logic for venuelook.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting accurate venue data requires navigating inconsistent formatting and dynamic page loads. Here is how we maintain pipeline stability.
We route requests through Indian residential IPs to avoid geo-blocking and rate limits while scraping local venue listings.
Many pricing tiers and capacity details on Venuelook load dynamically. We use Playwright to execute JavaScript and capture the fully rendered DOM.
Venue pages often have inconsistent layouts depending on the property type. Our selectors use multiple fallback chains to ensure data extraction continues even if a specific CSS class changes.
Search results rely on infinite scroll. Our crawlers simulate user scrolling and intercept XHR requests to ensure complete capture of all listings in a locality.
We maintain a hash index of previously scraped venues. Subsequent runs only output records where pricing or capacity details have changed, reducing downstream processing.
Hotels and banquet halls track local per-plate pricing and rental fees to optimise their own event packages.
Event management companies identify under-served localities by analysing venue density and average ratings.
Marketplaces build secondary directories by extracting profiles of photographers, decorators, and caterers.
B2B suppliers (F&B distributors, furniture renters) identify new and high-capacity venues for targeted outreach.
Analysts correlate commercial venue density and rental pricing with local property values for investment models.
Corporate travel and event teams feed venue capacity and pricing data into internal ERPs for automated shortlisting.
"Venuelook holds the most granular pricing and capacity data for India's unorganised event space market — essential for any hospitality analytics pipeline."
Extracting venue data requires navigating inconsistent formatting, infinite scroll search results, and dynamic pricing widgets. DataFlirt handles the proxy rotation, JavaScript rendering, and schema normalisation so your team can focus on market analysis instead of scraper maintenance.
Everything supported by our venuelook.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 handles JavaScript rendering and infinite scroll traversal.
We maintain pools of Indian residential ISP proxies. Rotation happens per-request to prevent IP bans and rate limiting.
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 venuelook.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Venuelook is generally permissible under applicable law. DataFlirt targets only public, non-authenticated venue, pricing, and vendor data. We do not bypass OTP walls or extract private user data.
We use Playwright to simulate user scrolling behaviour and intercept the underlying XHR/API requests, ensuring we capture all listings in a locality rather than just the initial page load.
We can configure pipelines to run daily, weekly, or monthly. Full city-wide refreshes typically complete within a 4-8 hour window depending on the target volume.
No. Venuelook gates direct contact numbers behind lead generation forms that require OTP verification. We only extract contact information if it is published openly in the venue description text.
Yes. We can target any city, locality, or region listed on the Venuelook platform.
Our smallest packages start at defined city lists (e.g., all venues in Delhi NCR) with monthly delivery. Contact us with your specific geographic and category requirements for a quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of banquet halls in Mumbai or a continuous price-monitoring feed across India — we scope, build, and operate the pipeline. Tell us what you need.
