We extract restaurant profiles, menu items, delivery fees, Grubhub+ status, and reviews. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
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 grubhub.com. All fields typed and schema-versioned.
"restaurant_id": "334921", "name": "Joe's Pizza", "rating": 4.8, "review_count": 1240, "delivery_fee": 2.99, "is_grubhub_plus": true, "latitude": 40.7306, "longitude": -74.0021
| # | restaurant_id | name | url | address | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Menus & Categories objects from grubhub.com. All fields typed and schema-versioned.
"item_id": "item_9921", "category_name": "Signature Pies", "item_name": "Pepperoni Slice", "base_price": 4.5, "currency": "USD", "popular_badge": true, "dietary_tags": "['Contains Dairy', 'Contains Gluten']"
| # | restaurant_id | category_name | item_id | item_name | description | base_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Item Modifiers objects from grubhub.com. All fields typed and schema-versioned.
"modifier_group_name": "Choose your crust", "modifier_name": "Gluten Free", "extra_price": 3.0, "is_required": true, "min_selections": 1, "max_selections": 1
| # | item_id | modifier_group_name | modifier_name | extra_price | is_required | min_selections |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from grubhub.com. All fields typed and schema-versioned.
"review_id": "rev_88321", "rating": 5, "review_text": "Best pizza in the village. Arrived hot.", "date_posted": "2023-10-12", "helpful_votes": 12, "order_items_referenced": "['Pepperoni Slice', 'Garlic Knots']"
| # | restaurant_id | review_id | author_name | rating | review_text | date_posted |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Fees & Logistics objects from grubhub.com. All fields typed and schema-versioned.
"delivery_fee": 2.99, "service_fee_pct": 11.5, "small_order_fee": 2.0, "pickup_available": true, "delivery_available": true, "tax_rate": 8.875
| # | restaurant_id | delivery_fee | service_fee_pct | small_order_fee | tax_rate | pickup_available |
|---|---|---|---|---|---|---|
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Our Grubhub scraper targets the complete restaurant catalogue. We parse complex modifier trees, track dynamic delivery fees, and simulate hyper-local GPS coordinates to capture accurate market data.
Categories, item names, descriptions, base prices, and dietary tags parsed directly from Grubhub restaurant endpoints.
Required selections, add-on pricing, and default options for highly customisable items are flattened into relational data.
Capture fluctuating delivery and service fees based on simulated user coordinates and cart sizes.
Identify restaurants participating in the Grubhub+ subscription program and track promotional eligibility.
Pass exact latitude and longitude headers to view location-specific search results and accurate delivery estimates.
Extract structured opening and closing times for delivery and pickup operations.
Pull aggregate scores, review counts, and individual customer feedback with timestamp data.
Track active discounts, free delivery offers, and minimum order requirements attached to specific restaurants.
Identify multiple virtual brands operating from a single physical address by normalising location coordinates.
Brief in. Clean data out.
Provide target zip codes, coordinates, or specific restaurant URLs. We design the extraction schema together.
We configure crawlers, proxy rotation, coordinate simulation, and Datadome bypass for grubhub.com.
Schema validation, null-rate checks, and modifier parsing verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed schedule.
Food delivery platforms invest heavily in scraping detection and API obfuscation. Here is how we maintain reliable pipelines.
Grubhub restricts visibility based on user location. We inject precise latitude and longitude coordinates into the request headers to render accurate local search results and delivery fees.
Menu data loads via complex GraphQL queries. We bypass the frontend rendering entirely and intercept the raw API responses for faster, structured extraction of deep modifier trees.
Grubhub uses Datadome for bot mitigation. We route requests through residential proxy pools and spoof TLS fingerprints to maintain high success rates and prevent IP bans.
Restaurant menus use deeply nested modifier logic. We flatten these hierarchical JSON structures into queryable relational formats ready for SQL analysis.
We hash menu items and prices per restaurant. Subsequent runs only emit records when prices, availability, or fees change, reducing your storage costs.
Food delivery aggregators monitor Grubhub pricing to maintain parity and optimise their own fee structures.
Real estate and logistics teams identify dense clusters of virtual restaurants operating from single commercial kitchens.
Restaurant groups analyse local competitors to price new menu items and identify underserved cuisine categories.
Analysts track how delivery and service fees fluctuate during peak hours across different metropolitan areas.
Operations teams map restaurant density and Grubhub+ adoption rates to plan new delivery zones.
Hedge funds track restaurant churn and new additions to model Grubhub market share against competitors.
"Grubhub holds the hyper-local pricing and menu data for over 300,000 restaurants, but extracting it requires mimicking exact GPS coordinates and bypassing strict bot protection."
Most teams underestimate the complexity of food delivery scraping. Grubhub requires precise coordinate spoofing, GraphQL query interception, and residential proxy networks to avoid Datadome blocks. DataFlirt absorbs that infrastructure overhead so your engineers can focus on market analysis, not maintenance.
Everything supported by our grubhub.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.
We bypass expensive browser rendering by intercepting Grubhub GraphQL endpoints directly, increasing extraction speed and reliability.
We route traffic through US-based residential IP pools to match the expected geographic footprint and avoid Datadome rate limits.
Pipelines run on Kubernetes with Airflow scheduling. We handle retries, proxy rotation, and data normalisation automatically.
Data delivered to where your team already works — no new tooling required.
About grubhub.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public restaurant and menu data is generally permissible. We do not bypass login walls, extract personal user data, or violate authentication terms. Clients should consult legal counsel for their specific use cases.
We accept a list of target coordinates or zip codes. Our crawlers simulate these locations using request headers to extract the exact menu, fees, and delivery times a local user would see.
Yes. We parse the full modifier tree, including required choices, maximum selections, incremental pricing, and default options.
We utilise residential proxies, TLS fingerprinting, and automated solvers to navigate Datadome restrictions without triggering blocks.
We normalise addresses and group multiple virtual restaurant entities that share the exact same physical coordinates.
For continuous tracking, we push pricing and availability diffs hourly. Full market sweeps typically complete within 24 hours depending on the geographic scope.
20-minute scoping call. Pilot dataset within the week. Production within two. From single-city menu dumps to national competitor price tracking, we build and operate the infrastructure. Tell us your target coordinates.
