Platform · Uber Eats Japan

Uber Eats Japan Data Scraping

Japanese quick commerce does not run on dark stores. It runs through convenience stores and supermarkets that were already there.

Uber Eats Japan data scraping collects restaurant menus, convenience-store and grocery listings, pricing and availability across Japanese cities. What makes Japan structurally different from every other quick-commerce market we cover: fulfilment runs through existing retail — convenience stores, supermarkets and drugstores — rather than purpose-built dark stores. A schema designed around dark stores will model the wrong thing.

Every other quick-commerce page in this project assumes a dark store behind the listing. Japan is the market where that assumption is simply wrong.

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ubereats_jp.jsonl LIVE FEED

{"platform":"uber_eats_jp", "fulfilment_type":"retail_partner", "store_chain":"convenience-chain-a","store_id":"cvs-4412", "ward":"Shibuya-ku","district":"Example district", "store_opening_hours":"24h","store_open":true, "item_name_ja":"as published", "item_price":298,"currency":"JPY", "in_stock":false, "unavailability_cause":"not_attributed", "note":"may be sold out to WALK-IN trade. a dark-store schema cannot express this"} {"fulfilment_type":"restaurant", "item_price":980, "min_realisable_price":1180, "note":"required selection adds 200"} {"address_id":"TKY-013", "panel_unit":"address_point", "caution":"most streets have no name. ward is a LABEL, not the panel unit"}

3 of 3,204,110 store-item rows · Japanfulfilment_type on every row · NOT a dark-store schema · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Uber Eats Japan or its owners. Uber Eats Japan and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Uber Eats Japan at a glance

How we handle Uber Eats Japan specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Uber Eats Japan — the larger of the two main players
The structural difference: Retail-partner fulfilment, not dark stores
Consequence: A store record is a real shop, with its own opening hours and catchment
Partners: Convenience stores, supermarkets and drugstores
Addressing: Japanese addresses are not street-based. Panels need designing differently
Market: Concentrated in Tokyo, Osaka, Yokohama and other dense metros
Menu text: Japanese retained as published. No translation into the record
Refresh: Daily standard; sub-daily where availability is the question

Platform specifics

What retail-partner fulfilment changes

These are the reasons a Uber Eats Japan dataset needs its own handling rather than a shared retail schema.

A store record here is an actual shop

In India, the Gulf and most of Europe, a quick-commerce listing sits behind a dark store — a facility that exists only to fulfil app orders, with no walk-in trade and no published hours.

Japan's model is different. Platforms partner with convenience store, supermarket and drugstore chains and deliver from branches that are already trading.

The store has real opening hours, and availability follows them.
The assortment is the shop's assortment, not a curated delivery range, so it is wider and more variable.
Stock reflects walk-in trade too, which means availability moves for reasons that have nothing to do with app demand.
The same branch appears on more than one platform, frequently.

So we record fulfilment_type as retail_partner, capture store_opening_hours where published, and treat a store being closed as a distinct state from an item being out of stock. A dark-store schema has nowhere to put any of that.

Which makes availability harder to interpret, and we say so

An item unavailable at 7pm in a convenience store may be sold out to walk-in customers rather than to app orders. We deliver the observation and do not attribute a cause.

Japanese addressing breaks a postcode or pincode panel

Japanese addresses are not street-based. They work by ward, district, block and building number, and most streets have no name at all.

That has direct consequences for panel design:

A postcode-equivalent panel does not map to delivery areas the way it does in the UK or Germany.
Ward and district are the usable administrative units, and they vary enormously in size and density.
Delivery radii cut across them, as they do everywhere.

We build address-point panels with coordinates, record ward and district as labels, and state the panel design in the deliverable — the same approach as our European metro coverage, with the difference that here there is no credible administrative unit to fall back on at all.

Text

Japanese menu and product names are retained exactly as published. We do not machine-translate into the name field. Matching runs on identifiers, structure and price rather than title similarity, which performs badly across a script where the same item can be written in kanji, hiragana or katakana.

Market context worth knowing before you scope

Two facts about this market shape how a panel should be built, and they are publicly reported rather than our opinion.

It is effectively a two-platform market

Uber Eats and Demae-can hold the large majority of share between them. A third international operator, Wolt, withdrew from Japan in March 2026 after six years.

For a competitive panel that is useful: two platforms covers most of the market, which is unusual. In most countries we would be arguing for four or five.

Coverage is metro-concentrated

Service is concentrated in Tokyo, Osaka, Yokohama and comparable metros. Outside those, coverage thins substantially and a national panel would be mostly empty.

We scope city panels rather than national ones here, and we say what the panel does and does not represent — the same discipline as our India coverage page.

Scope

What we collect on Uber Eats Japan, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Restaurant, convenience-store and grocery listings with fulfilment type recorded
store_opening_hours where published, with closed as a distinct state
Address-point panels with ward and district as labels, not as the unit
Modifier groups and minimum realisable price on restaurant items
Japanese names retained exactly, with no translation into the name field
Delivery, service and small-order fees as separate fields
Serviceability distinct from store closed and from out of stock
The same branch recorded separately per platform where both are in scope
Panel design stated, with what it represents and what it does not

❌ What we do not, and why

A dark-store schema applied to retail-partner fulfilment
A cause attributed to an item being unavailable
Machine translation written into name fields
A national panel where coverage is metro-concentrated
Sales, order volumes, courier or customer data

Core Uber Eats Japan fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
platform / fulfilment_type	retail_partner or restaurant. The distinguishing field
store_id / store_chain / ward / district	A real shop, and where it is
store_opening_hours / store_open	Published hours, and state at observation
address_id	Panel point with coordinates. Wards are labels, not the unit
item_name_ja	Retained exactly as published
item_price / currency	As displayed
modifier_groups / min_realisable_price	Restaurant items
in_stock / unavailability_cause	Observed, and cause not attributed
delivery_fee / service_fee / small_order_fee	Each separately
serviceable	Distinct from store closed and from out of stock
observed_at / daypart	Timestamp and derived daypart

Use cases

What teams do with Uber Eats Japan data

Two-platform competitive panel

Uber Eats and Demae-can on one address panel, which covers most of this market — unusual, since most countries need four or five platforms for comparable coverage.

Convenience-store assortment and pricing

Retail-partner listings with store chain and opening hours, showing what konbini and supermarket branches actually offer through delivery rather than what a dark store was stocked with.

Restaurant menu pricing at realisable level

Modifier groups with minimum realisable price, so two restaurants listing identically are not treated as equivalent.

Metro panel design for a non-street address system

Address-point panels with ward and district as labels, which is the only workable approach where most streets have no name.

The 24-hour sample — run on your sources, not ours

Send us a Uber Eats Japan item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Uber Eats Japan is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Uber Eats Japan data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what food & restaurant data covers, and a Uber Eats Japan-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Uber Eats Japan data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because a store record here is an actual shop with real opening hours, walk-in trade and the shop's own assortment — not a facility that exists only to fulfil app orders.

A dark-store schema has nowhere to put opening hours, and it treats a closed shop as a stockout. We record fulfilment_type and keep store-closed distinct from out-of-stock.

No. In a convenience store an item unavailable at 7pm may be sold out to walk-in customers rather than to app orders.

We deliver the observation and do not attribute a cause, because the cause is not visible from the listing.

Because Japanese addresses are not street-based — they work by ward, district, block and building, and most streets have no name. A postcode-equivalent panel does not map to delivery areas the way it does in the UK or Germany.

We build address-point panels with coordinates and use ward and district as labels rather than as the unit.

Two, which is unusual. Uber Eats and Demae-can hold the large majority of share between them, and a third international operator withdrew from Japan in March 2026.

In most countries we would be arguing for four or five. Here two gets you most of the market.

Not into the name field. Matching runs on identifiers, structure and price rather than title similarity, which performs badly across a script where the same item can be written in kanji, hiragana or katakana.

Translation is available as a separate field with the engine and date recorded.

We quote individually. Coverage is metro-concentrated, so we scope city panels rather than national ones — a national panel would be mostly empty outside the major metros.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Uber Eats Japan data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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