Food Delivery Platform Data Extraction — Uber Eats & DoorDash (USA)

Client Overview

The client required a complete, structured view of the U.S. food-delivery landscape — restaurants and their full menu catalogues from two major platforms simultaneously — to support in-depth market analysis of restaurant availability, menu diversity, pricing patterns, and regional distribution trends.

Actowiz Solutions executed a large-scale, once-off extraction across Uber Eats and DoorDash spanning the entire United States, delivering standardised JSON files via Amazon S3. Each restaurant record carried a Google Places PlaceID as a mandatory cross-platform location identifier, menu item deep-link URLs built from validated numeric IDs, and a UTC crawl timestamp for freshness and traceability.

The Challenge

• Scale of coverage. Both platforms operate across hundreds of U.S. cities and zip codes, with listings and menus varying by location — full national coverage demanded a systematic geographic crawl strategy.
• Two-platform extraction. Uber Eats and DoorDash have distinct site architectures and data structures, requiring independent pipelines that still produce one unified output schema.
• Menu depth. Coverage extended to all categories, sub-categories, and individual items with pricing, descriptions, and image URLs where available.
• Mandatory Google Places PlaceID. Each restaurant needed a stable cross-platform PlaceID extracted from platform data (external Google Maps enrichment was out of scope).
• Menu item IDs & deep links. Item IDs required as numeric values derived from URL structures, with deep-link URLs generated from those validated IDs.
• Image URL handling. Item image URLs included where available, using a blank string (not null) when absent, for schema consistency.
• Crawl timestamp. A UTC timestamp at the restaurant level (not item level) to track data freshness.
• Structured JSON via S3. All data serialised as clean, validated JSON and delivered through S3 for direct ingestion.

The Solution by Actowiz Solutions

Geographic Coverage Strategy

A systematic location-based crawl achieved national coverage. Restaurants were identified and extracted across all available cities and service areas on both platforms, capturing location-level variation in listings and menus for an accurate snapshot of each platform's footprint.

Restaurant-Level Extraction

For each restaurant, the pipeline collected all available attributes. Google Places PlaceID was treated as mandatory and sourced from platform-available data. A UTC crawl timestamp was appended per record at extraction time. Phone number and website URL were captured where available via Google Places data (availability depends on underlying coverage).

Menu-Level Extraction

Complete catalogues were extracted per restaurant — all categories, sub-categories, and items. Item IDs were standardised as numeric-only values from URL structures, and deep-link URLs generated from them. Image URLs were included where present (blank string when absent). Pricing and descriptions were captured as displayed.

Dual-Platform Architecture

• Uber Eats — independent pipeline built against Uber Eats' page structure and rendering logic, with location-based crawl coverage.
• DoorDash — separate pipeline tailored to DoorDash's architecture, with equivalent menu depth and attribute coverage.
• Unified schema — all data normalised into one consistent JSON schema, eliminating platform-specific transformation.
• S3 delivery — final validated JSON files delivered to the client's designated S3 bucket on completion.

Technical Approach

• Dual-platform pipeline. Independent engines for Uber Eats and DoorDash, each handling platform-specific rendering, dynamic loading, and session management.
• National geographic crawl. Systematic coverage of full U.S. supply on both platforms via location-based strategies.
• Menu depth extraction. Full hierarchy traversal per restaurant — categories, sub-categories, items, pricing, images, IDs.
• ID normalisation. Item IDs standardised to numeric-only across platforms, with deep-link URLs generated from validated IDs.
• PlaceID extraction. Place IDs sourced from platform-embedded data; no external Google Maps API calls in scope.
• JSON serialisation & validation. Normalised, validated against the agreed schema, and serialised before S3 delivery within the 5–6 working-day timeline.

Results & Business Impact

• National market visibility. A complete structured snapshot across both platforms — availability, category mix, pricing, ratings — queryable by geography for regional analysis and territory planning.
• Menu intelligence. Item-level pricing, descriptions, and image URLs enabled competitive menu benchmarking and visual content-quality assessment.
• Cross-platform location linking. Mandatory PlaceID let the client link records to Google Maps, review platforms, and CRM systems without fragile address matching — and refresh specific records by PlaceID without re-crawling.
• Data freshness & traceability. Per-record UTC crawl timestamps let the client filter or weight data by recency.
• Integration-ready delivery. JSON via S3 was immediately ingestible; the unified schema removed platform-specific transformation.

Client Feedback

"Actowiz handed us the entire U.S. food-delivery map — 120,000+ restaurants with full menus across both platforms, one clean schema. The Place IDs linked straight into our systems. We were querying by region within a day."

— Head of Market Intelligence

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