We extract restaurant listings, full menu hierarchies, user reviews, dish ratings, and delivery estimates from Zomato. 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 Restaurant Profiles objects from zomato.com. All fields typed and schema-versioned.
"restaurant_id": "18234512", "name": "Meghana Foods", "city": "Bengaluru", "locality": "Koramangala", "rating_dining": 4.5, "rating_delivery": 4.3, "average_cost": 800, "is_zomato_gold": true
| # | restaurant_id | name | city | locality | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Menus & Pricing objects from zomato.com. All fields typed and schema-versioned.
"restaurant_id": "18234512", "category": "Biryani", "item_name": "Chicken Boneless Biryani", "price": 315.0, "currency": "INR", "is_veg": false, "is_bestseller": true, "votes": 1423
| # | restaurant_id | category | item_id | item_name | description | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from zomato.com. All fields typed and schema-versioned.
"review_id": "rev_984123", "restaurant_id": "18234512", "user_name": "Rahul Sharma", "user_level": "Connoisseur", "rating": 5.0, "review_text": "Best biryani in Koramangala. The chicken is perfectly cooked.", "likes_count": 14, "visit_date": "2026-04-12"
| # | review_id | restaurant_id | user_id | user_name | user_level | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Offers objects from zomato.com. All fields typed and schema-versioned.
"restaurant_id": "18234512", "is_delivering_now": true, "delivery_time_mins": 35, "delivery_fee": 45.0, "offers_available": true, "discount_text": "Flat 100 off on orders above 499", "min_order_value": 149.0, "distance_km": 3.2
| # | restaurant_id | is_delivering_now | delivery_time_mins | delivery_fee | offers_available | discount_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search Results objects from zomato.com. All fields typed and schema-versioned.
"keyword": "pizza", "locality": "Indiranagar", "position": 1, "restaurant_id": "54321", "name": "Brik Oven", "sponsored": false, "rating": 4.6, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | city | locality | position | restaurant_id | name |
|---|---|---|---|---|---|---|
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Our Zomato scraper handles precise location spoofing, API token rotation, and deep menu traversal to deliver structured restaurant intelligence without hitting pagination blocks or IP bans.
Name, location, precise coordinates, operational timings, contact info, and facility tags extracted at scale.
Full category-item trees with prices, descriptions, and dietary flags like veg/non-veg and spice levels.
Separate extraction for dining ratings and delivery ratings, including total vote counts and rating distributions.
Full text, user levels, timestamps, and image URLs from the review section to gauge customer sentiment.
Extract Zomato Gold status, flat discounts, and bank-specific promo codes visible on the restaurant profile.
Capture real-time delivery estimates, live tracking availability, and dynamic delivery fees based on injected coordinates.
Precise latitude and longitude coordinates for spatial analysis, routing, and density mapping.
Identify paid placements versus organic search results in specific localities to track competitor ad spend.
Run daily or weekly pipelines to track menu price inflation and identify new restaurant additions automatically.
Brief in. Clean data out.
Provide city lists, locality URLs, or specific restaurant IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, coordinate proxy rotation, and manage API token generation for zomato.com.
Schema validation, null-rate checks, price-outlier detection, and sample menus before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Zomato uses heavy API pagination, dynamic tokens, and location-based blocks. Here is how we maintain stable extraction.
Zomato restricts visibility based on user location. We inject precise lat/long coordinates into the request headers and Playwright context to see exactly what a local user sees.
Zomato's frontend relies on short-lived API tokens. Our pipeline intercepts and refreshes these tokens automatically, bypassing the need to render heavy DOM elements for every page.
Search results often cap at a few hundred items. We use geospatial grid search and micro-locality filtering to extract the complete restaurant catalogue without hitting pagination walls.
We route requests through ISP-grade residential proxies in India and the UAE to prevent IP bans and rate limiting from Zomato's security edge.
Zomato frequently updates its internal API response structures. We use schema-validation middleware to detect field changes and fallback to alternative endpoints instantly.
Analyze cuisine gaps, average order values, and delivery ratings across micro-markets to identify new locations.
F&B brands track competitor pricing, inflation trends, and discount strategies at the dish level.
FMCG and restaurant groups mine user reviews to gauge customer satisfaction and dish-specific feedback.
Compare delivery times, fees, and restaurant overlaps against competing platforms.
B2B suppliers extract contact details and facility tags to pitch POS systems, ingredients, and packaging.
Correlate restaurant density and average cost metrics with commercial real estate values.
"Zomato holds the definitive map of the urban food economy, but extracting hyper-local menus and pricing at scale requires precise coordinate spoofing and API reverse-engineering."
Relying on manual data entry or basic scrapers fails when Zomato updates its token authentication or blocks datacenter IPs. DataFlirt handles the location injection, proxy rotation, and payload parsing so your data science team can focus on market analysis instead of pipeline maintenance.
Everything supported by our zomato.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 location spoofing, API token interception, and interaction flows.
We maintain pools of residential ISP proxies across target regions. 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 Postgres.
Data delivered to where your team already works — no new tooling required.
About zomato.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Zomato is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant, menu, and review data. We do not extract personal data or circumvent authentication walls.
We inject exact latitude and longitude coordinates into our request headers and Playwright contexts. This ensures we extract the exact delivery times, fees, and restaurant visibility that a user at that location would experience.
Yes. We traverse the entire menu hierarchy, capturing category names, item names, descriptions, prices, dietary flags, and high-resolution image URLs.
Real-time streaming pipelines achieve sub-60-minute latency for specific restaurant sets. Full city-wide refreshes at daily cadence complete within an 8-hour window.
We support extraction across all active Zomato markets, including India and the UAE. Our proxy infrastructure routes requests locally to avoid geo-blocks.
No. We extract the public review text, user display name, rating, and timestamp. We do not extract private contact information or email addresses.
Our smallest packages start at a defined locality list or 1,000 specific restaurant IDs with weekly delivery. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off restaurant dump or a continuous price-monitoring feed across multiple cities, we scope, build, and operate the pipeline. Tell us what you need.
