We extract restaurant profiles, dynamic table availability, menus, and verified reviews from OpenTable. 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 opentable.com. All fields typed and schema-versioned.
"restaurant_id": "r-12345", "name": "Gordon Ramsay Steak", "city": "Las Vegas", "price_band": "$$$$", "cuisine": "Steakhouse", "rating_overall": 4.8, "review_count": 14592
| # | restaurant_id | name | url | address | neighbourhood | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Table Availability objects from opentable.com. All fields typed and schema-versioned.
"restaurant_id": "r-12345", "requested_date": "2026-10-15", "party_size": 2, "time_slot": "19:30", "seating_type": "Standard", "experience_name": "Tasting Menu", "experience_price": 250.0
| # | restaurant_id | scrape_timestamp | requested_date | requested_time | party_size | available_slots |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from opentable.com. All fields typed and schema-versioned.
"review_id": "rev-9876", "rating_overall": 5, "rating_food": 5, "rating_service": 4, "rating_noise": "Moderate", "review_text": "Exceptional steak.", "recommended": true
| # | review_id | restaurant_id | diner_name | dining_date | rating_overall | rating_food |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Menus objects from opentable.com. All fields typed and schema-versioned.
"restaurant_id": "r-12345", "menu_name": "Dinner", "category": "Mains", "item_name": "Beef Wellington", "price": 75.0, "currency": "USD", "updated_at": "2026-05-12T00:00:00Z"
| # | restaurant_id | menu_name | category | item_name | item_description | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search Results objects from opentable.com. All fields typed and schema-versioned.
"keyword": "Italian", "location": "Manhattan", "position": 3, "restaurant_id": "r-999", "promoted": false, "rating": 4.6, "available_times": "['18:00', '18:30']"
| # | keyword | location | date | time | party_size | position |
|---|---|---|---|---|---|---|
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Our OpenTable scraper handles every layer of the platform: restaurant metadata, dynamic availability polling, menu structures, and verified diner reviews - with automated GraphQL interception and anti-bot circumvention built in.
Name, location, coordinates, operating hours, chef details, and parking rules extracted for every listed venue.
Extract open time slots, seating types (bar, standard, outdoor), and waitlist status for specific party sizes and dates.
Overall, food, service, and ambience ratings, noise level metrics, and full text from authenticated diners.
Menu categories, item names, descriptions, and prices across lunch, dinner, and brunch menus.
Track special ticketed events, tasting menus, and prepaid experiences with associated pricing data.
Track search result rankings, promoted restaurant flags, and category visibility for specific neighbourhoods.
Extract data from OpenTable US, UK, Canada, Australia, and all supported international domains.
Map restaurants to specific neighbourhoods, cross-streets, and transit hubs using OpenTable location tags.
Run availability checks at minute-level intervals to track booking velocity and sell-out rates.
Brief in. Clean data out.
Provide city URLs, neighbourhood lists, or specific restaurant IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for opentable.com.
Schema validation, null-rate checks, availability-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
OpenTable relies on complex GraphQL queries and aggressive bot detection. Here is how we maintain stable extraction at scale.
OpenTable uses sophisticated bot protection to block automated traffic. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS spoofing to bypass these blocks.
OpenTable renders dynamic availability via complex GraphQL network requests. Instead of parsing the DOM, our Playwright infrastructure intercepts these XHR requests to extract raw JSON, ensuring high fidelity and speed.
Time slots vanish in milliseconds during peak booking windows. Our concurrent architecture captures availability snapshots accurately across multiple party sizes and dates without triggering rate limits.
Availability and pricing can change based on IP origin. We route requests through locale-specific proxy pools to ensure data matches the experience of a local diner.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing time slots, schema drift, and coverage drops - and respond before you notice.
Restaurant groups track pricing, menu changes, and review sentiment across rival venues to optimise their own operations.
Investors use booking velocity and availability density as a proxy for neighbourhood economic activity and footfall.
Hospitality platforms ingest availability data to optimise their own yield management systems and dynamic pricing algorithms.
Analysts track cuisine trends, price band shifts, and new openings across major metropolitan areas.
Hedge funds monitor reservation volumes at publicly traded restaurant chains to forecast quarterly earnings.
B2B food suppliers identify highly-rated, high-volume restaurants for targeted outreach and sales campaigns.
"OpenTable holds the real-time pulse of global dining demand, but extracting availability grids requires intercepting dynamic GraphQL queries at scale."
Most teams fail at OpenTable scraping because they rely on slow DOM parsing. We intercept the underlying GraphQL network requests, handle bot challenges via residential proxies, and map complex availability nodes into flat, warehouse-ready schemas. DataFlirt absorbs the infrastructure burden so your analysts can query the data immediately.
Everything supported by our opentable.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.
Playwright handles JavaScript execution and intercepts GraphQL network requests. Scrapy parses the structured JSON responses, bypassing fragile DOM selectors entirely.
We maintain pools of residential ISP proxies across target regions. Rotation happens per-request with sticky sessions where required to bypass aggressive bot mitigation.
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 opentable.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from OpenTable is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant metadata, availability, and reviews. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review OpenTable's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. For availability data, we intercept GraphQL requests directly. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
Yes. We configure pipelines to poll availability grids for specific dates, times, and party sizes. This data is extracted and delivered as structured time-series records.
Yes. We extract full menu structures, including categories, item descriptions, prices, and dietary tags across lunch, dinner, and brunch menus where available.
Real-time streaming pipelines achieve sub-15-minute latency for availability signals on a defined restaurant set. Full city-wide metadata refreshes at daily cadence complete within a 6-hour window.
We support opentable.com, opentable.co.uk, opentable.com.au, opentable.ca, and other regional domains from a unified schema with currency normalisation.
Yes. We track SERP positions for specific keywords and locations, distinguishing between organic results and promoted/sponsored placements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off restaurant catalogue dump or a continuous availability-monitoring feed across 10,000 venues - we scope, build, and operate the pipeline. Tell us what you need.
