We extract restaurant profiles, 30-point rating matrices, curated review snippets, and location data from Zagat. 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 zagat.com. All fields typed and schema-versioned.
"restaurant_id": "ZGT-84921", "name": "Le Bernardin", "cuisine": "['French', 'Seafood']", "city": "New York", "price_tier": "$$$$", "phone": "+1 212-555-0198", "zip_code": "10019"
| # | restaurant_id | name | url | cuisine | address | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Zagat Ratings objects from zagat.com. All fields typed and schema-versioned.
"restaurant_id": "ZGT-84921", "food_score": 29, "decor_score": 28, "service_score": 29, "cost_estimate": 185.0, "zagat_review": "Magical seafood preparations in a formal, hushed setting...", "featured_list": "['Top Food', 'Most Popular']"
| # | restaurant_id | food_score | decor_score | service_score | cost_estimate | zagat_review |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for User Reviews objects from zagat.com. All fields typed and schema-versioned.
"review_id": "REV-993821", "user_name": "J. Smith", "user_rating": 5, "review_text": "Impeccable service and the tasting menu is flawless.", "review_date": "2026-03-14", "helpful_votes": 42
| # | review_id | restaurant_id | user_name | user_rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location & Hours objects from zagat.com. All fields typed and schema-versioned.
"latitude": 40.7614, "longitude": -73.9818, "neighborhood": "Midtown West", "monday_hours": "12:00 PM - 2:30 PM, 5:15 PM - 10:30 PM", "weekend_hours": "5:15 PM - 11:00 PM", "timezone": "America/New_York"
| # | restaurant_id | latitude | longitude | neighborhood | monday_hours | tuesday_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Features & Amenities objects from zagat.com. All fields typed and schema-versioned.
"outdoor_seating": false, "full_bar": true, "wheelchair_accessible": true, "parking_type": "Valet", "dress_code": "Jacket Required", "private_dining": true
| # | restaurant_id | outdoor_seating | reservations_required | full_bar | wheelchair_accessible | parking_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Zagat scraper captures the complete 30-point rating system, editorial reviews, and operational metadata with full JavaScript rendering to navigate dynamic search interfaces.
Capture granular scores for Food, Decor, and Service across the classic 30-point Zagat scale.
Extract Zagat's signature editorial snippets, combining user quotes with professional critique.
Extract precise latitude, longitude, and neighbourhood boundaries for spatial analysis.
Map cost estimates and price tiers to standardise dining expenses across markets.
Extract and normalise cuisine arrays, from broad categories to specific regional styles.
Parse complex daily schedules, including split shifts for lunch and dinner service.
Capture features like outdoor seating, full bar availability, parking types, and dress codes.
Aggregate community feedback, ratings, and helpful votes from paginated review sections.
Run one-off bulk exports or configure continuous pipelines with change-detection for rating updates.
Brief in. Clean data out.
Provide target cities, cuisine types, or specific restaurant URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for zagat.com.
Schema validation, null-rate checks, and rating outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from modern single-page applications requires specialised infrastructure. Here is how we maintain pipeline stability.
Zagat's interface relies heavily on client-side routing and API hydration. We run full Playwright browser sessions to execute JavaScript, trigger lazy-loaded reviews, and capture map-based search results.
To prevent IP bans and rate limiting, our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing, trained on real user behaviour patterns.
We use multiple fallback chains per field, including CSS selectors, XPath, and structured data extraction (LD+JSON), ensuring a layout change does not break your data pipeline.
For large restaurant catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and schema drift, responding before you notice.
Restaurant groups monitor regional ratings, pricing, and sentiment to benchmark their properties against local leaders.
Commercial real estate firms analyse dining density and quality scores to evaluate neighbourhood vitality and retail potential.
F&B analysts track cuisine popularity, price inflation, and service trends across major metropolitan areas.
ML teams use Zagat's structured rating matrices and editorial reviews to train sentiment analysis and recommendation models.
Concierge services and travel platforms integrate Zagat scores to curate premium dining recommendations for users.
Private equity firms evaluate restaurant group portfolios by analysing historical rating stability and customer sentiment.
"Zagat's 30-point rating system remains the gold standard for restaurant evaluation, but accessing that matrix programmatically requires dedicated extraction infrastructure."
Extracting structured data from modern single-page applications requires more than simple HTTP GET requests. DataFlirt handles the JavaScript rendering, residential proxy rotation, and schema maintenance required to turn Zagat's editorial content into a reliable, queryable database.
Everything supported by our zagat.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About zagat.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Zagat is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant profiles, ratings, and reviews. We do not extract personal data or circumvent authentication walls.
We use full Playwright browser sessions to execute JavaScript, interact with map elements, and capture the JSON responses that populate the dynamic search results.
Full catalogue refreshes at weekly or monthly cadences complete within defined windows. We can also configure targeted daily runs for specific high-priority markets.
Yes. We capture the complete 30-point rating matrix, breaking down the overall score into its constituent Food, Decor, and Service components.
Our packages start at a defined list of cities or restaurant URLs. For custom schema requirements across nationwide datasets, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 restaurant profiles as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of a specific city or a continuous feed of national restaurant ratings, we scope, build, and operate the pipeline. Tell us what you need.
