Zomato Restaurant Search Scraper: Extract Dining Data from India's Leading Food Platform

Why Zomato Data Matters for Food Industry Intelligence

Zomato dominates India's online food delivery and restaurant discovery market, serving millions of users across hundreds of cities. The platform aggregates critical data about restaurants, their offerings, pricing, promotions, and customer preferences—making it an essential resource for anyone in the food and beverage industry.

For restaurant owners, aggregators, market researchers, and business analysts, Zomato data reveals dining trends, competitive positioning, promotional strategies, and consumer behavior patterns. However, manually collecting this information across multiple locations and restaurant categories would be impractical. This scraper automates that process, delivering structured data ready for analysis.

What This Scraper Extracts

The Zomato Restaurant Search Scraper collects detailed information from search result pages, capturing everything from basic restaurant details to promotional offers and delivery options. It processes dine-out, delivery, and takeaway listings, extracting both visible information and underlying metadata that powers Zomato's recommendation algorithms.

Target users include food delivery platforms conducting competitive analysis, restaurant chains monitoring market presence, investors researching F&B trends, and data analysts building predictive models for the hospitality sector.

Input Configuration Explained

The scraper accepts Zomato search page URLs—these are the results pages you see after searching for restaurants in a specific area or category. The URL structure typically includes location parameters and filters.

Input Parameters:

{
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [],
    "apifyProxyCountry": "US"
  },
  "max_items_per_url": 20,
  "ignore_url_failures": true,
  "urls": [
    "https://www.zomato.com/ncr/dine-out-in-paharganj?place_name=New%20Delhi%20Railway%20Station,%20%20New%20Delhi%20Railway%20station,%20%20Pahar%20Ganj"
  ]
}

Example Screenshot:

max_items_per_url limits how many restaurants to extract per search URL, useful for controlling costs and focusing on top results. ignore_url_failures ensures the scraper continues processing other URLs even if one fails, important when scraping multiple locations. The urls array accepts multiple search pages, allowing you to collect data across different neighborhoods, cuisines, or dining types simultaneously.

Note that proxy usage may be necessary for large-scale scraping to avoid rate limits, though it's disabled in this example for simplicity.

Complete Output Structure and Field Definitions

The scraper returns JSON data with each restaurant as an object containing detailed fields. Understanding field purposes enables effective analysis and application.

Restaurant Identification

ID: Unique numeric identifier of the restaurant on Zomato. Purpose: Database indexing, tracking specific restaurants over time, avoiding duplicates.

Name: Name of the restaurant. Purpose: Display, lead generation, brand and competitor analysis.

Type: Type of the listed entity (e.g., restaurant). Purpose: Separating restaurants from other entity types in mixed datasets.

Restaurant URL: Direct link to the restaurant page on Zomato. Purpose: Accessing full details, menus and reviews, sharing with clients, verifying scraped data.

Image URL: Link to the main restaurant photo. Purpose: Visual display in reports and dashboards.

Featured Image URL: Link to the featured (banner) photo of the restaurant. Purpose: Higher-quality visuals for marketing materials and listings.

Ratings and Reviews

Rating: Overall aggregate rating of the restaurant. Purpose: Quality benchmarking, ranking, filtering top-rated venues.

Rating Label: Text label of the rating (e.g., Very Good). Purpose: Human-readable quality segmentation.

Votes: Total number of reviews/votes behind the overall rating. Purpose: Measuring popularity and rating reliability.

Is New Restaurant: Whether the restaurant is flagged as newly opened. Purpose: Identifying new market entrants.

Has Fake Reviews: Platform indicator of suspected fake reviews (0 = none flagged). Purpose: Assessing review trustworthiness and data quality.

Dining Rating: Average rating from dine-in experiences. Purpose: Evaluating on-premise experience quality.

Dining Review Count: Number of dine-in reviews. Purpose: Judging reliability of the dining rating.

Delivery Rating: Average rating from delivery orders. Purpose: Evaluating delivery service quality.

Delivery Review Count: Number of delivery reviews. Purpose: Judging reliability of the delivery rating and delivery activity level.

Pricing

Cost For Two: Average cost for two people as displayed. Purpose: Price-tier segmentation, budget filtering, market positioning.

Cost For One: Average cost for one person as displayed (if available). Purpose: Per-person price comparison and affordability analysis.

Cuisine

Cuisines: List of cuisine types served. Purpose: Menu categorization, market segmentation, cuisine trend analysis.

Cuisine URLs: Links to Zomato listing pages for each cuisine, in the same order as Cuisines. Purpose: Exploring competitors in the same cuisine category.

Location and Availability

Locality: Neighborhood and city of the restaurant. Purpose: Geographic segmentation and local market analysis.

Address: Street address of the restaurant. Purpose: Mapping, field visits, local outreach.

Locality URL: Link to the Zomato listing of restaurants in the same locality. Purpose: Competitor discovery and area-level analysis.

Distance: Distance from the search reference point. Purpose: Proximity filtering and location-based ranking.

Opening Status: Opening/closing status at scrape time (e.g., "Closes in 59 minutes"). Purpose: Checking real-time availability. Note: time-sensitive snapshot.

Promotion and Offers

Is Promoted: Whether the listing appears as a paid promotion. Purpose: Analyzing advertising investment by competitors.

Promoted Text: Promotion label displayed on the listing. Purpose: Verifying how the promotion is presented.

Promo Offer: Promotional offer text shown on the listing (may be empty). Purpose: Tracking discounts and marketing campaigns.

Has Bulk Offers: Whether bulk offers are available. Purpose: Identifying group or volume deals.

Bulk Offers: List of bulk offer details (empty if none). Purpose: Analyzing offer structures.

Order Options: List of online ordering options (empty if none). Purpose: Assessing delivery and ordering capabilities.

Gold Offers: List of Zomato Gold membership offers (empty if none). Purpose: Identifying restaurants participating in loyalty programs.

Takeaway Options: List of takeaway options (empty if none). Purpose: Assessing takeaway availability.

Is Disabled: Whether the listing is disabled on the platform. Purpose: Filtering out inactive listings.

Source

Source URL: Page on which the restaurant was found during scraping. Purpose: Traceability, understanding the search context.

Raw Data

Raw Data: Complete original JSON object of the restaurant as scraped, without modification. Purpose: Auditing, debugging, accessing fields not included in the cleaned output, and reprocessing without re-scraping.

Example output structure:

{
  "id": 18454488,
  "name": "The GT Road",
  "type": "restaurant",
  "restaurant_url": "https://www.zomato.com//ncr/the-gt-road-connaught-place-new-delhi/info",
  "image_url": "https://b.zmtcdn.com/data/pictures/8/18454488/4188a5acb8e537010cb899dd879160d7_featured_v2.jpg?output-format=webp",
  "featured_image_url": "https://b.zmtcdn.com/data/pictures/8/18454488/357698cb4873d2c2333945618e97fab6_o2_featured_v2.jpg?output-format=webp",
  "rating": "4.2",
  "rating_label": "Very Good",
  "votes": "4,687",
  "is_new_restaurant": false,
  "has_fake_reviews": 0,
  "dining_rating": "4.4",
  "dining_review_count": "4,667",
  "delivery_rating": "4.0",
  "delivery_review_count": "20",
  "cost_for_two": "₹2,400 for two",
  "cost_for_one": "₹1,000 for one",
  "cuisines": [
    "North Indian",
    "Mughlai",
    "Bar Food",
    "Desserts",
    "Beverages"
  ],
  "cuisine_urls": [
    "https://www.zomato.com/ncr/restaurants/north-indian/",
    "https://www.zomato.com/ncr/restaurants/mughlai/",
    "https://www.zomato.com/ncr/restaurants/bar-food/",
    "https://www.zomato.com/ncr/restaurants/desserts/",
    "https://www.zomato.com/ncr/restaurants/beverages/"
  ],
  "locality": "Connaught Place, New Delhi",
  "address": "M 39, Outer Circle, Connaught Place, New Delhi",
  "locality_url": "https://www.zomato.com//ncr/connaught-place-delhi-restaurants",
  "distance": "1.5 km",
  "opening_status": "Closes in 54 minutes",
  "is_promoted": true,
  "promoted_text": "Promoted",
  "promo_offer": "",
  "has_bulk_offers": false,
  "bulk_offers": [],
  "order_options": [],
  "gold_offers": [],
  "takeaway_options": [],
  "is_disabled": false,
  "source_url": null,
  "raw_data": null,
  "from_url": "https://www.zomato.com/ncr/dine-out-in-paharganj?place_name=New%20Delhi%20Railway%20Station,%20%20New%20Delhi%20Railway%20station,%20%20Pahar%20Ganj"
}

Using the Scraper Effectively

Create an Apify account and access the Zomato scraper. Identify your target search URLs by browsing Zomato and filtering by location, cuisine, or dining type. Copy the resulting URLs into your input configuration.

Configure max_items_per_url based on your needs—20 captures top restaurants, while higher values provide broader market coverage. Enable ignore_url_failures when scraping multiple locations to ensure partial failures don't halt the entire run.

For large-scale operations across multiple cities, organize URLs by region and run separate scraping jobs to manage data more effectively. Set up scheduled runs to track changes in promotions, ratings, or new restaurant additions over time.

Monitor execution through the Apify console. Processing speed depends on the number of URLs and items per URL, typically handling 100-200 restaurants within 5-10 minutes.

Practical Applications

Competitive Intelligence: Restaurant owners can monitor competitor promotions, pricing changes, and service offerings in their area. Track which restaurants invest in promoted listings and how promotion frequency correlates with customer engagement.

Market Research: Analyze cuisine trends by geography, identify underserved neighborhoods, and understand pricing patterns across different dining categories. The promotional data reveals seasonal campaign strategies and discount intensity by market segment.

Delivery Platform Analytics: Aggregators can benchmark against Zomato's offerings, identify restaurants not yet on competing platforms, and understand partnership penetration rates across different localities.

Investment Analysis: Investors researching F&B opportunities can assess market density, pricing dynamics, and competitive intensity in target locations. Gold membership participation rates indicate restaurant willingness to join loyalty programs.

Pricing Strategy: Cross-reference cost-for-two data with ratings and order volumes to identify optimal pricing positions. Track how promotional intensity varies by location and restaurant type.

Best Practices

Schedule regular scraping to capture promotional cycles and rating changes. Most restaurants update offers weekly, making bi-weekly data collection optimal for trend analysis.

Combine Zomato data with other platforms (Swiggy, Google Maps) for comprehensive market intelligence. Cross-validate restaurant information across sources to identify discrepancies.

Implement data quality checks for anomalies in ratings, pricing, or availability status. Set up alerts for significant changes like new competitors entering your market or major promotional campaigns.

Store historical data to analyze seasonal patterns, track restaurant lifecycle (new openings to closures), and measure how promotional strategies evolve over time.

Respect rate limits and use proxies for large-scale operations to ensure sustainable access to this valuable data source.

Conclusion

The Zomato Restaurant Search Scraper transforms India's largest food platform into actionable business intelligence. Whether you're optimizing restaurant operations, conducting market research, or building competitive strategies, this tool provides the structured data needed for informed decision-making in the dynamic food delivery and dining sector.