We extract restaurant listings, menu prices, delivery fees, operating hours, and promotional tags from Glovo. 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 Store Listings objects from glovo.com. All fields typed and schema-versioned.
"store_id": "s8d9f2", "name": "Burger King", "category": "Burgers", "rating": 4.6, "rating_count": 1205, "delivery_fee": 1.99, "prep_time_minutes": 25, "is_prime": true, "is_active": true
| # | store_id | name | category | sub_category | rating | rating_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Menu Categories objects from glovo.com. All fields typed and schema-versioned.
"category_id": "c4592", "store_id": "s8d9f2", "name": "Value Meals", "position": 1, "item_count": 14, "is_promotional": true, "active_status": true
| # | category_id | store_id | name | description | position | item_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Menu Items objects from glovo.com. All fields typed and schema-versioned.
"item_id": "i99234", "store_id": "s8d9f2", "name": "Whopper Meal", "price": 8.5, "discounted_price": 7.5, "available": true, "has_options": true, "popular_badge": true
| # | item_id | store_id | category_id | name | description | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Item Customisations objects from glovo.com. All fields typed and schema-versioned.
"option_group_id": "og773", "item_id": "i99234", "name": "Choose your drink", "is_required": true, "min_selection": 1, "max_selection": 1, "choices": "['Coca Cola', 'Fanta', 'Water']", "additional_price": 0.0
| # | option_group_id | item_id | name | is_required | min_selection | max_selection |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Delivery & Fees objects from glovo.com. All fields typed and schema-versioned.
"store_id": "s8d9f2", "location_lat": 41.3851, "location_lng": 2.1734, "delivery_fee": 3.49, "surge_pricing_active": true, "bad_weather_fee": 1.0, "estimated_time": "30-40 min"
| # | store_id | location_lat | location_lng | delivery_fee | service_fee | surge_pricing_active |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Glovo scraper handles geospatial grid mapping, mobile API emulation, and dynamic fee structures to extract clean restaurant and grocery data across any operating city.
Inject precise latitude and longitude coordinates to map available restaurants, dark stores, and pharmacies within specific delivery radii.
Capture categories, items, prices, descriptions, and dietary tags. We extract deeply nested option groups and add-on pricing matrices.
Monitor delivery fees, service fees, and surge pricing indicators tied to specific coordinates and timeframes.
Extract store ratings, review counts, estimated preparation times, and minimum order values for every listed vendor.
Scrape Glovo Express, supermarkets, and retail partners with thousands of SKUs, capturing stock availability and promotional pricing.
Identify stores participating in Glovo Prime and extract active promotional banners, percentage discounts, and 2-for-1 offers.
Extract detailed weekly schedules and temporary closure statuses based on kitchen load or courier availability.
Extract data from Spain, Italy, Romania, Kenya, and 20+ other markets with automated currency and language normalisation.
Run continuous pipelines to track menu changes, out-of-stock items, and fee fluctuations at hourly or daily cadences.
Brief in. Clean data out.
Provide target cities, coordinate grids, or specific store URLs. We design the extraction schema for your use case.
We configure geospatial crawlers, mobile API emulators, proxy rotation, and session management for Glovo's endpoints.
Schema validation, coordinate overlap deduplication, and price-outlier detection before full production launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Glovo's local-first architecture means data only exists at specific coordinates. Here is how we bypass location blocks and anti-bot systems.
Glovo does not have a global directory. Stores are only visible if you provide a delivery address. We generate overlapping hexagonal coordinate grids for target cities, injecting precise lat/long payloads into API requests to discover every available vendor.
The Glovo web app is heavily rate-limited and obfuscated. We reverse-engineer and target their mobile application APIs directly, handling complex header generation, TLS fingerprinting, and session token rotation to extract clean JSON payloads.
Restaurant menus feature deeply nested logic: mandatory choices, multi-select add-ons, and conditional pricing. Our schema normalises these complex option groups into flat, queryable records suitable for relational databases.
Stores frequently toggle off during peak hours or bad weather. We track these state changes, distinguishing between permanent closures, out-of-operating-hours, and temporary high-load pauses.
Glovo uses advanced edge protection to block datacenter IPs and anomalous request patterns. We route traffic through localised residential proxies matching the target country, rotating IPs and session tokens per coordinate request.
Ghost kitchens and restaurant groups track local pricing, menu additions, and promotional strategies to remain competitive.
Rival aggregators and logistics companies monitor Glovo's dynamic delivery and service fees across different postcodes and weather conditions.
FMCG and beverage brands track product availability, pricing, and category positioning within Glovo Express and partner supermarkets.
Private equity firms and analysts map restaurant penetration and dark store expansion to evaluate geographic market share.
Marketing teams track the frequency of 2-for-1 offers, percentage discounts, and Glovo Prime participation rates in specific neighbourhoods.
Franchise operators audit their own store listings to ensure menu consistency, correct pricing, and proper image usage across regions.
"Glovo's local-first architecture means data only exists at specific coordinates. You cannot scrape it without a geospatial strategy."
Extracting quick-commerce data requires simulating mobile app API calls with precise latitude and longitude payloads. DataFlirt handles the geospatial grid mapping, session tokens, and proxy rotation so your analysts can query clean menu data without fighting location blocks.
Everything supported by our glovo.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 generate overlapping hexagonal grids for target cities, ensuring complete coverage of delivery zones without redundant coordinate requests.
Our pipelines bypass web frontends to interface directly with mobile APIs, handling TLS fingerprinting and dynamic token generation.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About glovo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Glovo is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant, menu, and pricing data. We do not extract personal user data, circumvent authentication walls, or violate GDPR. Clients should review Glovo's ToS and consult legal counsel for specific use cases.
We use a geospatial grid strategy. You provide target cities or polygons, and we generate a grid of latitude/longitude coordinates. Our crawlers inject these coordinates into API requests to discover all stores serving those specific locations.
We support all markets where Glovo operates, including Spain, Italy, Romania, Poland, Kenya, Morocco, and Ivory Coast. We route requests through country-specific residential proxies to ensure accurate local pricing and availability.
We can configure pipelines to run daily for full catalogue refreshes, or at higher frequencies (e.g., hourly) for specific high-value stores to track dynamic delivery fees and surge pricing.
Yes. Our schema flattens deeply nested menu structures, capturing required choices, optional add-ons, size variations, and additional pricing logic associated with each base item.
Our minimum engagement typically starts with mapping a single major city or tracking a defined list of 1,000+ specific store URLs. We price based on geographic scope, delivery frequency, and data volume.
Absolutely. We provide a sample run of up to 50 stores in a specified location as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off city map or a continuous price-monitoring feed across thousands of restaurants — we scope, build, and operate the pipeline. Tell us what you need.
