# Dotlas Data Catalog > A trusted & comprehensive hub for retail, real estate, and hospitality industries for market & economic data by delivering actionable insights through precise and updated data products. The Dotlas Data Catalog documents ready-to-use market datasets for the restaurant, hotel, and geospatial sectors, and the Dotlas MCP, a live agent-facing query interface over the same data. # AI Access (MCPs) # AI Access (MCPs) > Connect an AI assistant to Dotlas data and ask questions in plain language. The **Dotlas MCP** is a server that lets an AI assistant, such as Claude, query Dotlas restaurant market data directly. Instead of downloading a dataset and loading it into your warehouse, you connect your AI client once and ask questions in natural language. The assistant calls Dotlas **tools** to fetch live answers about brands, menus, pricing, delivery, promotions, and search visibility. MCP stands for [**Model Context Protocol**](https://www.anthropic.com/news/model-context-protocol), an open standard for connecting AI assistants to external tools and data. Access is granted per organization The Dotlas MCP is available to enabled organizations. If your organization is not set up yet, [contact us](https://dotlas.com) to request access. Once enabled, any member can connect a client and start asking. ## How it works You ask your assistant a question. The assistant decides which Dotlas tool answers it, sends the tool a few structured arguments, and gets back a read-only result. Your conversation stays in your client. Dotlas only ever receives the arguments a tool needs. ``` flowchart LR A[You] --> B[AI assistant] B -->|calls a tool| C[Dotlas MCP] C -->|read-only query| D[(Dotlas data)] D -->|result| C C -->|answer| B B --> A ``` ## Dataset download vs. the MCP The MCP does not replace dataset delivery. It is a second way to reach the same data, suited to questions rather than bulk analysis. | | Dataset download | Dotlas MCP | | -------------------- | ------------------------------------- | ------------------------------------- | | **How you get data** | Bulk file to your warehouse | Ask a question, get an answer | | **Best for** | Large-scale analysis, joins, modeling | Lookups, exploration, agent workflows | | **Freshness** | Snapshot at delivery | Live at query time | | **Who uses it** | Data teams | Anyone with an AI client | ## What you can ask The tools are grouped into six families. A few examples: - **Discovery** ______________________________________________________________________ Find brands and cuisines, and see the markets you can access. [What you can ask](https://catalog.dotlas.com/ai_access/tools/index.md) - **Menus & pricing** ______________________________________________________________________ Read a brand's menu shape, prices, discounts, and price history. [What you can ask](https://catalog.dotlas.com/ai_access/tools/index.md) - **Geography & outlets** ______________________________________________________________________ Map a brand's footprint, find nearby outlets, and see competitors. [What you can ask](https://catalog.dotlas.com/ai_access/tools/index.md) - **Delivery, search & promotions** ______________________________________________________________________ Track delivery KPIs, search ranking, promotions, and openings. [What you can ask](https://catalog.dotlas.com/ai_access/tools/index.md) ## Next steps - **Connect a client** ______________________________________________________________________ Set up Claude or any MCP client in a few steps. [Connect](https://catalog.dotlas.com/ai_access/connect/index.md) - **What you can ask** ______________________________________________________________________ The full list of tool families and example questions. [Explore the tools](https://catalog.dotlas.com/ai_access/tools/index.md) - **Security & trust** ______________________________________________________________________ Read-only access, per-organization scoping, and a clear data boundary. [Review security](https://catalog.dotlas.com/ai_access/security/index.md) Ready to try it? Enabled organizations can head straight to [Connect a client](https://catalog.dotlas.com/ai_access/connect/index.md). Everyone else can [contact us](https://dotlas.com) to request access. # Connect a Client Connecting takes a minute. You point your AI client at the Dotlas MCP endpoint, authenticate, and pick your organization. After that, your assistant can use the Dotlas tools in any chat. Before you start Your organization must be enabled for the MCP, and you need an account that belongs to it. If you are not enabled yet, [contact us](https://dotlas.com) to request access. ## The endpoint Point your client at this URL. It is a standard streamable-HTTP MCP server served at the bare root — there is no path to append. ``` https://mcp.dotlas.com ``` Every request is authenticated. Most clients sign in with **OAuth** (a browser sign-in and organization selection); headless and command-line clients can use an **access key** instead. Choose your method below, then jump to the [recipe for your client](#recipes). ## Two ways to authenticate You only ever use **one** of these — never both. Each recipe below lets you pick between them. **OAuth — recommended.** You sign in through your browser and select an organization on the consent screen. It works in every client, including claude.ai on the web, and nothing sensitive is stored in a config file. The organization you pick scopes the data you can query, so choose the one that holds your Dotlas access. **Access key.** A `dl_live_` bearer key that Dotlas issues you, sent as an `Authorization: Bearer ` header. Use it for headless or command-line agents (Codex, CI jobs, scripts) or any client where a browser sign-in is awkward. A key is **read-only** and **scoped to your organization's access**, so there is no organization-selection step. [Contact us](https://dotlas.com) to get one. Treat an access key like a password A key does not expire on its own. Never commit it to source control or leave it in shell history — keep it in an environment variable or your client's secret store, and [contact us](https://dotlas.com) to rotate or revoke it if it is ever exposed. ## Connection recipes Pick your client below. Where a client supports both sign-in methods, switch between the **OAuth** and **Access key** tabs and follow just one — you do not need both. 1. Open your connector settings in Claude and choose to add a **custom connector**. 1. Paste the endpoint `https://mcp.dotlas.com` and confirm. 1. When prompted, **sign in** through the browser window that opens. 1. **Select your organization** on the consent screen. This scopes every query to that organization's licensed markets and brands. 1. Return to Claude. The Dotlas tools now appear in the connector. Menu labels vary by client version Exact button and menu names change between releases. The flow is always the same: add a custom connector, use the endpoint above, sign in, and select your organization. Using an access key on Claude Desktop claude.ai on the **web** supports OAuth only. **Claude Desktop** can instead add a request header under the custom connector's advanced settings (beta): `Authorization: Bearer dl_live_…`. Opens a browser so you can sign in and select your organization: ``` claude mcp add --transport http dotlas https://mcp.dotlas.com ``` Then, inside Claude Code, run `/mcp`, choose **dotlas**, and complete the sign-in. No browser — for headless use: ``` claude mcp add --transport http dotlas https://mcp.dotlas.com \ --header "Authorization: Bearer dl_live_xxx" ``` Add `--scope user` to either command to make the server available in every project rather than just the current one. Add the server to `~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (per project). Cursor opens a browser to sign in on first use: ``` { "mcpServers": { "dotlas": { "url": "https://mcp.dotlas.com" } } } ``` `${env:DOTLAS_MCP_KEY}` reads the key from your environment so it stays out of the file: ``` { "mcpServers": { "dotlas": { "url": "https://mcp.dotlas.com", "headers": { "Authorization": "Bearer ${env:DOTLAS_MCP_KEY}" } } } } ``` In Copilot agent mode, add the server to `.vscode/mcp.json`. VS Code prompts you to sign in on first use: ``` { "servers": { "dotlas": { "type": "http", "url": "https://mcp.dotlas.com" } } } ``` Declare an input so VS Code prompts for the key once and stores it securely, then send it as a header: ``` { "inputs": [ { "type": "promptString", "id": "dotlas-key", "description": "Dotlas MCP access key", "password": true } ], "servers": { "dotlas": { "type": "http", "url": "https://mcp.dotlas.com", "headers": { "Authorization": "Bearer ${input:dotlas-key}" } } } } ``` Codex connects with an access key. Add the server to `~/.codex/config.toml`. Reading the key from an environment variable keeps it out of the file: ``` [mcp_servers.dotlas] url = "https://mcp.dotlas.com" bearer_token_env_var = "DOTLAS_MCP_KEY" enabled = true ``` Or set the header inline: ``` [mcp_servers.dotlas] url = "https://mcp.dotlas.com" http_headers = { "Authorization" = "Bearer dl_live_xxx" } enabled = true ``` Run the inspector, then connect from its UI: ``` npx @modelcontextprotocol/inspector ``` 1. Set **Transport Type** to `Streamable HTTP`. 1. Enter the URL `https://mcp.dotlas.com`. 1. Complete the OAuth sign-in, **or** paste your access key into the **Bearer Token** field. 1. Click **Connect**. The Dotlas tools are listed under **Tools**. Any compliant MCP client connects the same way: 1. Add a new remote / custom server with the endpoint `https://mcp.dotlas.com`. 1. Authenticate — complete the OAuth sign-in in your browser (the server advertises its OAuth metadata automatically), or send an access key as an `Authorization: Bearer ` header. 1. Select your organization when prompted (OAuth only), and the Dotlas tools appear. ## Verify it works Ask your assistant a question that uses a tool, such as *"Which markets can I access?"* A short list of your licensed markets confirms the connection and that your organization scope is correct. ## Troubleshooting I get a 401 or the connection is refused The server rejects requests that are not signed in. With OAuth, complete the browser sign-in and make sure you selected an organization. With an access key, check the header is exactly `Authorization: Bearer dl_live_…` with no extra spaces. If authentication succeeds but the connection is still rejected, your organization may not be enabled yet — [contact us](https://dotlas.com) to check. No tools appear after connecting This usually means your organization is not enabled for the MCP. [Contact us](https://dotlas.com) and we will confirm your access. The data looks empty or wrong With OAuth, you may have selected the wrong organization at sign-in — disconnect, reconnect, and pick the organization that holds your Dotlas access. Results are always limited to the markets and brands your organization is licensed for. My access key stopped working A key is revoked the moment it is rotated. If you regenerated or reported a key, [contact us](https://dotlas.com) for the current one, and update the header or environment variable in your client's config. ## Next Now see [what you can ask](https://catalog.dotlas.com/ai_access/tools/index.md), or review [security and trust](https://catalog.dotlas.com/ai_access/security/index.md) before you roll it out to your team. # What You Can Ask The Dotlas MCP exposes a set of read-only **tools**, grouped below into six families. You do not call them directly. You ask your assistant a question in plain language, and it picks the right tools and threads them together. This page shows what each family covers and gives example questions. The connected client always shows the current tool list This page describes what the tools do. Your MCP client displays the live, authoritative list of tools and their parameters the moment you connect. If the two ever differ, trust the client. ## How the tools connect Most questions start by identifying a brand. `search_brands` returns a `brandSlug`, the identifier that the menu, pricing, geography, delivery, and feed tools use to look up that brand. You rarely see this. The assistant fetches the slug and passes it along for you. ``` flowchart LR Q[Your question] --> S[search_brands] S -->|brandSlug| M[Menu & pricing] S -->|brandSlug| G[Geography & outlets] S -->|brandSlug| D[Delivery & search] S -->|brandSlug| F[Promotions & feeds] ``` ## Discovery Find brands by name or cuisine, see which cuisines a market has, list the markets your organization can access, and pull a single brand's profile (logo, colours, socials, outlet count, virtual-brand ratio). **Tools:** `search_brands`, `get_cuisines`, `get_markets`, `get_brand_profile` - *"Which markets can I access?"* - *"Find McDonald's in the UAE."* - *"Which brands serve sushi in Dubai?"* ## Menus & pricing Read a brand's menu shape, price levels, and discount coverage, then drill into a section, a single item's weekly price history, its price on each source, and its menu-change timeline. **Tools:** `get_menu_sections`, `get_section_menu_items`, `get_item_price_history`, `get_item_source_prices`, `get_item_menu_events` - *"What does Shake Shack's menu look like in Saudi Arabia: sections, price levels, discount coverage?"* - *"Has the price of the classic burger changed over the last three months?"* - *"What does this item cost on each delivery app?"* ## Geography & outlets Map a brand's footprint, list the outlets in a city or area (with each outlet's rating and review count), find outlets near a point, discover mapped neighborhoods, read per-outlet popular-times busyness, see Dotlas's computed competitor list for a brand — now with the per-dimension similarity breakdown (price, location, menu, search, size) behind the ranking — and rank the brands whose outlets most densely surround a brand's footprint (the market-radar watch set beyond its named competitors). **Tools:** `get_brand_outlets`, `get_nearby_outlets`, `get_neighbourhoods`, `get_competitor_suggestions`, `get_radar_brands`, `get_popular_times` - *"Where are all of Starbucks' outlets in Dubai?"* - *"Which restaurants are near this outlet?"* - *"Who competes with this brand?"* - *"Which brands cluster around this brand's outlets?"* ## Delivery & search visibility See a brand's delivery fees, times, and distances by zone — alongside the competitor average and the brand-vs-competitor deltas — the search terms tracked on delivery apps in a market, how a brand ranks over time for a term in a given neighborhood, and a current-vs-previous snapshot of its search rank and visibility share per term and neighborhood. **Tools:** `get_delivery_performance`, `get_distinct_search_queries`, `get_search_ranking_history`, `get_search_visibility` - *"What are this brand's delivery fees and times across Dubai?"* - *"Which search terms are tracked on delivery apps in the UAE?"* - *"How does this brand rank for 'burger' in this neighborhood over time?"* ## Promotions, events & social Track a brand's promotions and ad campaigns as one timeline (ad-campaign rows now carry the creative — media URLs, caption, CTA type — with an optional per-creative gallery grain), map the competitor offer landscape across the neighborhoods a brand operates in, list the menu items that changed recently, see where a brand opened new outlets, and read its recent social posts and engagement. **Tools:** `get_promotions_feed`, `get_offer_landscape`, `get_menu_event_types`, `get_items_with_recent_events`, `get_outlets_feed`, `get_social_media_feed` - *"What promotions is this brand running?"* - *"Which menu items changed recently?"* - *"Where has this brand opened new outlets this year?"* ## Reviews & customer sentiment Read a brand's review volume, sources, and rating snapshot with precomputed topic and highlight enrichment; pull newest-first verbatim reviews filtered by topic, sentiment, or text search; and pulse a set of nearby competitor brands for their aggregate topic sentiment — to see whether rivals share a brand's complaints or win where it loses (aggregate only, never verbatim rival reviews; coverage is Dotlas's analysed brand set). **Tools:** `get_review_summary`, `get_brand_reviews`, `get_competitor_review_pulse` - *"What are customers praising and complaining about for this brand?"* - *"Show me recent reviews mentioning slow service."* - *"Do nearby rivals suffer the same complaints as this brand, or win where it loses?"* What the MCP does not cover Every figure is competitive intelligence from public delivery-app and web data — never a client's own operational data. The tools do not return ad spend or impressions, order volume, sales, revenue, or basket size, and distances are straight-line, not routed delivery times. Ratings, review counts, and popular-times busyness are public popularity proxies, not demand. For the deeper datasets, see the [restaurant catalog](https://catalog.dotlas.com/restaurants/index.md) or [contact us](https://dotlas.com). # Security & Trust ## What crosses the boundary When your assistant answers a question, only the **structured arguments** of a tool call reach Dotlas: a brand identifier, a country code, a search keyword, and similar. The rest of your conversation stays with you and your AI client. Dotlas never receives the chat. ``` flowchart LR subgraph yourside["Your side"] U[You] LLM[AI assistant] C[[Full conversation]] end subgraph dotlas["Dotlas"] MCP[Dotlas MCP] DB[(Restaurant data)] end U <--> LLM C -.->|never sent| MCP LLM -->|"structured arguments only"| MCP MCP -->|"read-only results"| LLM MCP --> DB ``` Stated precisely A keyword you type can travel inside a tool argument, for example a brand name you asked about. So the accurate statement is that Dotlas receives only the structured arguments a tool needs, never the full conversation. ## Read-only by design Every Dotlas tool is a query. Connecting the MCP grants read access to Dotlas data and nothing more. - **No writes** ______________________________________________________________________ The tools cannot create, change, or delete any data, on your side or ours. - **No irreversible actions** ______________________________________________________________________ There are no destructive operations in the toolset. Nothing to undo. - **No access to your systems** ______________________________________________________________________ The server cannot execute code or reach into your files, network, or applications. ## Scoped to your organization Every query is scoped to the organization you select at sign-in. You only ever see the markets and brands your organization is licensed for. One organization cannot see another's data. ## Authentication Access uses OAuth. You sign in through your identity provider and select an organization. The server requests least-privilege scopes: your profile, your email, and the ability to read your organization membership so it can scope your data. Requests that are not signed in, or that do not select an organization, are rejected. ## Questions from a security review Can it change or delete our data? No. Every tool is read-only. There are no write, update, or delete operations. Does Dotlas see our whole conversation? No. Only the structured arguments of a tool call reach Dotlas, never the surrounding chat. Can it access our internal systems? No. The server answers data queries. It cannot execute code or reach your files, network, or applications. Who in our organization can connect? Any member of an enabled organization who signs in and selects that organization. If your organization is not enabled, [contact us](https://dotlas.com). How are queries logged or retained, and where is data hosted? For specifics on logging, retention, and hosting suitable for a formal review, [contact us](https://dotlas.com) and we will share the current details. ## Next Ready to set it up? See [Connect a client](https://catalog.dotlas.com/ai_access/connect/index.md), or browse [what you can ask](https://catalog.dotlas.com/ai_access/tools/index.md). # Restaurants # Restaurants ## Catalog - **Unified Restaurant Establishments** ______________________________________________________________________ A single unified dataset that brings together point-of-interest (PoI) and rich metadata about restaurants from multiple online and offline sources into a single set of linked records. [View Data Products](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/index.md) - **Restaurant Menus & Pricing** ______________________________________________________________________ Cross-source conflated menu data for judging availability, pricing and menu events for price optimization and comparison. [View Data Products](https://catalog.dotlas.com/restaurants/menus/index.md) - **Restaurant Activations & Promotions** ______________________________________________________________________ Discover the promotions, social presence and advertisements being launched by restaurants online. [View Data Products](https://catalog.dotlas.com/restaurants/activations/index.md) - **Customer Review Sentiments for Restaurants** ______________________________________________________________________ Customer review data and associated sentiment for restaurants, with split by dine-in and delivery. [View Data Products](https://catalog.dotlas.com/restaurants/customer_reviews/index.md) - **Delivery Times for Restaurants** ______________________________________________________________________ Access the average customer delivery times by brand, neighbourhood or outlet, with delivery radius and time-series based on ETAs. [View Data Products](https://catalog.dotlas.com/restaurants/delivery_times/index.md) ______________________________________________________________________ ## Special Datasets - **Michelinguide Restaurants** ______________________________________________________________________ A monthly extract of Michelinguide's restaurant catalog to determine which countries, cities or cuisines are being considered favourably and which categories are retiring. [Learn More](https://catalog.dotlas.com/restaurants/michelinguide_restaurants/index.md) # Unified Restaurant Establishments with Rich Metadata Dotlas maintains a unified and collection of restaurant Points-of-Interest (PoIs) across multiple online and offline data sources linked together as a single-source-of-truth places dataset. This dataset consolidates and enriches records from delivery platforms, review aggregators, licensing authorities, brand directories, and proprietary field research using advanced entity resolution techniques. Each restaurant entity is matched across sources using a combination of geospatial proximity, name similarity, brand hierarchy, and contextual metadata, ensuring a high-confidence, non-redundant view of physical outlets and their associated brands. The end result is a ready to use collection of restaurant records with enhanced details from multiple sources as one. ### Potential Sectors for Use-Cases - Food & Beverages (F&B) - GIS (Geographic Information Systems) and Geocoding Services - Marketing and Advertising Leads - Competitor Monitoring & Market Analysis - Financial tickering of F&B brand performance - Franchise network tracking and portfolio management - Site Selection & Suitability Analysis - Real Estate Proximity Analysis ## Data Dictionary ### Standard Schema The following fields are available as persistent data points irrespective of which market or country is chosen | **Column Name** | **Data Type** | **Description** | | ---------------------- | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **restaurant_id** | `string` | Unique Dotlas-assigned identifier for the restaurant. Useful for tracking the same physical restaurant over time. | | **brand_id** | `string` | Unique Dotlas-assigned identifier for the chain / brand of the restaurant | | **brand_name** | `string` | The name of the chain or brand when applicable, else the name of the restaurant or outlet | | **country** | `string` | The country in which the restaurant is located | | **city** | `string` | The city where the restaurant is located | | **subregion** | `string` | The state / district / territory in which the restaurant is located. Example: `California`, `Normandy`, `Baden-Württemberg`, etc. | | **zipcode** | `string` | The postal code of the restaurant, if available | | **area** | `string` | The Dotlas-assigned colloquial neighbourhood of the restaurant, if available for our [supported urban boundaries](https://catalog.dotlas.com/maps/urban_boundaries/index.md). Example: `Downtown`, `Marina`, `Morningside Heights`, etc. | | **latitude** | `float` | The exact location coordinate of the restaurant (with customizable projections) | | **longitude** | `float` | The exact location coordinate of the restaurant (with customizable projections) | | **address** | `string` | The full address of the restaurant, including street, city, state, and postal code | | **rating** | `float` | The average rating of the restaurant, split by source if required | | **review_count** | `int` | The number of reviews for the restaurant, split by source if required | | **categories** | `string[]` | The cuisine tags that the restaurant lists itself for keyword search | | **restaurant_website** | `string` | The official website of the restaurant, if available | | **telephone_number** | `string` | The desk phone number of the restaurant, if available | | **email_address** | `string` | The email address of the restaurant, if available | | **instagram** | `string` | The Instagram handle of the restaurant, if available. Other social handles can be made available on request | ### Specific Schema The following fields can be made available for specific markets or countries, based on the sources available. | **Column Name** | **Data Type** | **Description** | | ------------------------ | ------------- | ----------------------------------------------------------------------------------------------------------------------- | | **description** | `string` | A description of the restaurant or brand from online sources or their own site | | **facilities** | `string[]` | The facilities available at the restaurant, such as `Wi-Fi`, `Parking`, `Outdoor Seating`, etc. | | **operating_hours** | `string` | The operating hours of the restaurant, including opening and closing times for each day of the week | | **dietary_restrictions** | `string[]` | The dietary restrictions that the restaurant caters to, such as `Vegetarian`, `Vegan`, `Gluten-Free`, etc. | | **delivery_options** | `string[]` | The delivery options available for the restaurant, such as `In-House`, `Third-Party`, `Pickup`, etc. | | **payment_methods** | `string[]` | The payment methods accepted by the restaurant, such as `Cash`, `Credit Card`, `Mobile Payment`, etc. | | **legal_entity** | `string` | The legal entity under which the restaurant brand operates, such as local franchising company names or parent companies | ### Bespoke Schema The following fields are made available for any market on-request using Dotlas market algorithms and economic transformation pipelines. Contact us for more details. | **Column Name** | **Data Type** | **Description** | | ------------------------ | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **restaurant_type** | `string` | A categorization of the restaurant based on natural language understanding of their menu offering, service type, and other factors. Example: `Fast Food`, `Casual Dining`, `Fine Dining`, etc. | | **outlet_dnps** | `float` | An NPS-like metric that measures the likelihood of visiting customers recommending the restaurant to others, based on sentiment analysis of reviews and social media mentions. See more info about [dNPS](https://catalog.dotlas.com/restaurants/customer_reviews/index.md) | | **nearby_competitors** | `string[]` | A list of nearby competitors for the restaurant, based on geospatial proximity and similarity to the current outlet's offerings by price, services, location and more. A list of `restaurant_id` are used to populate this field. | | **visitation_trends** | `float[]` | A time series of visitations to the restaurant over time, based on geospatial data and other sources. This can be used to track the performance of the restaurant over time and identify trends in customer behavior. | | **search_ranking_index** | `float` | A score that indicates the visibility of the restaurant in search results, based on its online presence, delivery apps, and other places where customers discover this restaurant. This can be used to track the performance of the restaurant in search engines and identify opportunities for improvement. | | **promotion_index** | `float` | A score that indicates the effectiveness of the restaurant's promotions and advertisements, based on customer engagement and conversion rates. This can be used to track the performance of the restaurant's marketing efforts and identify opportunities for improvement. | ## Products ### North America - 🇺🇸 **United States of America** ______________________________________________________________________ A collection of **over 1M restaurant venues** in the United States, across all states and territories with rich information around facilities, operating hours, contact details, and other attributes. [Access on Databricks](https://marketplace.databricks.com/details/dde5767b-8ba2-4e39-9efc-7b371470bd41/Dotlas_Restaurants-and-FB-Dataset-United-States-of-America-US-Sample) [Contact Us](https://dotlas.com) - 🇨🇦 **Canada** ______________________________________________________________________ A collection of **over 120k restaurant venues** in Canada, with information from multiple popular online sites and sources. [Contact Us](https://dotlas.com) ### Middle East - 🇦🇪 **United Arab Emirates** ______________________________________________________________________ A collection of **over 40k restaurant venues** in the UAE, including restaurants in Dubai, Abu Dhabi, Sharjah, etc. [Access on Databricks](https://marketplace.databricks.com/details/7f0ea6c6-90b5-4277-bb0c-dd67c51bd81f/Dotlas_Restaurants-in-the-United-Arab-Emirates-AE-Places-Data-Sample) [Contact Us](https://dotlas.com) - 🇧🇭 **Bahrain** ______________________________________________________________________ A collection of **over 5k restaurant venues** in Bahrain, including all governorates. [Access on Databricks](https://marketplace.databricks.com/details/c1a1923a-8b78-44e6-b16c-006695083039/Dotlas_Restaurants-Dataset-for-Bahrain-BH-Sample) [Contact Us](https://dotlas.com) - 🇰🇼 **Kuwait** ______________________________________________________________________ A collection of **over 12k restaurant venues** in Kuwait, including all major restaurant sources, delivery aggregators and other sources. [Access on Databricks](https://marketplace.databricks.com/details/30b5e732-6774-4688-a4e7-cb3346f89548/Dotlas_Restaurants-Dataset-for-Kuwait-KW-Sample) [Contact Us](https://dotlas.com) - 🇶🇦 **Qatar** ______________________________________________________________________ A collection of **over 8k restaurant venues** in Qatar, including all major restaurant sources, delivery aggregators in Doha, Lusail and other areas. [Access on Databricks](https://marketplace.databricks.com/details/4b56038c-34f6-4b3c-afa4-865ff120ffe6/Dotlas_Restaurants-Dataset-for-Qatar-QA-Sample) [Contact Us](https://dotlas.com) - 🇸🇦 **Saudi Arabia** ______________________________________________________________________ A collection of **over 45k restaurant venues** in Saudi Arabia, including all major restaurant sources, delivery aggregators in Jeddah, Riyadh, Dammam, etc. [Access on Databricks](https://marketplace.databricks.com/details/02797db9-86b1-4a3f-b7a5-818e25af3d3b/Dotlas_Restaurants-Dataset-for-Saudi-Arabia-SA-Sample) [Contact Us](https://dotlas.com) - 🗺️ **Additional Middle-Eastern Countries** ______________________________________________________________________ [🇴🇲 Oman Restaurant Sample on Databricks](https://marketplace.databricks.com/details/113c3685-cb6a-40d7-8289-c1fd68f49d80/Dotlas_Restaurants-in-Oman-OM-Places-Data-Sample) [🇯🇴 Jordan Restaurant Sample on Databricks](https://marketplace.databricks.com/details/12b5f2a7-c99b-4418-89e2-8ee9c60188bc/Dotlas_Restaurants-in-Jordan-JO-Places-Data-Sample) [🇪🇬 Egypt Restaurant Sample on Databricks](https://marketplace.databricks.com/details/ce2d0da8-877c-44a4-8768-7ba3fe072da8/Dotlas_Restaurants-in-Egypt-EG-Places-Data-Sample) [Contact Us](https://dotlas.com) ### Asia-Pacific - 🇮🇳 **India** ______________________________________________________________________ A collection of **over 270k restaurant venues** in India, including all major restaurant sources, across all major cities like Delhi, Mumbai, Bengaluru, Hyderabad and more. [Access on Databricks](https://marketplace.databricks.com/details/e6876a14-ea72-4ef7-8b98-f420f382f29f/Dotlas_Restaurants-in-India-IN-Places-Data-Sample) [Contact Us](https://dotlas.com) - 🇹🇼 **Taiwan** ______________________________________________________________________ A collection of **over 60k restaurant venues** in Taiwan, including all major restaurant sources, across major cities. [Contact Us](https://dotlas.com) ### More Markets - 🇧🇷 **Brazil** ______________________________________________________________________ A collection of **over 30k restaurant venues** in Brazil, including all major restaurant sources, across all major cities like Rio Je Janeiro, Sao Paulo, Curitiba, etc. [Access on Databricks](https://marketplace.databricks.com/details/7f0a7b9e-1fd5-4a2a-b2e1-7d1e90086e10/Dotlas_Restaurants-in-Brazil-BR-Places-Data-Sample) [Contact Us](https://dotlas.com) - 🗺️ **Other Countries** ______________________________________________________________________ Contact us if you're interested in dataset of other countries. Based on the sources available, the schema of new markets are quite likely similar to existing samples. [Contact Us](https://dotlas.com) ______________________________________________________________________ Don't see a listed country for which you require restaurant data? We're always expanding our library of off-the-shelf products, but we might still be able to provision efforts to gather the necessary data for your needs. Contact us for more details. # Restaurant Menus Info This dataset is available for any market, samples are listed for similar countries as [Unified Restaurant Establishments](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/index.md) Dotlas maintains a unified, structured collection of restaurant menus aggregated from multiple online and offline data sources. This dataset brings together dish-level and section-level information for restaurants, normalized across platforms like delivery apps, brand websites, menu scans, and proprietary field audits. Each menu is linked to a verified restaurant entity in the [Unified Restaurant Establishments](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/index.md) dataset and enriched with standardized food categories, inferred product names, and pricing information at both the individual source level and a computed average price across sources. The result is a powerful, analysis-ready dataset that enables clean comparisons of pricing, product assortment, and section structure across restaurants, brands, and neighborhoods. ## Data Dictionary - [Access on Databricks](https://marketplace.databricks.com/details/dde5767b-8ba2-4e39-9efc-7b371470bd41/Dotlas_Restaurants-and-FB-Dataset-United-States-of-America-US-Sample) All menu rows are linked to a restaurant and follow a consistent schema, filterable by `restaurant_id`, `item_name`, or other fields | **Column Name** | **Data Type** | **Description** | | ----------------------- | ------------- | -------------------------------------------------------------------------- | | **restaurant_id** | `string` | Unique Dotlas-assigned identifier for the restaurant | | **brand_id** | `string` | Unique Dotlas-assigned identifier for the chain / brand of the restaurant | | **item_name** | `string` | Name of the dish or menu item | | **item_description** | `string` | Free-text description of the dish as seen on the source menu | | **section_name** | `string` | The section of the menu the item belongs to (e.g. Appetizers, Main Course) | | **section_description** | `string` | Any description available for the section as a subtitle in the menus | | **menu_name** | `string` | Lunch or Dinner Menu / Drinks Menu or A La Carte, etc. | | **menu_description** | `string` | A description of the menu if available | | **avg_price** | `float` | Average price of the item across multiple observed sources | Tip This is a snapshot dataset, which means menus can be captured on a periodic basis for markets on a subscription to measure price changes, new or retired items and more over time. This would involve an additional `timestamp` field to see menus as a time-series. ## Additional Menu Fields Available on Request - **Product Information & Categorization** ______________________________________________________________________ Use our enhanced attributes which detail the product name and product category with sizing. This data is especially useful for comparing menu items by product in the market and judging average cost for a basket of items. Additional hierarchical categorization can also be made available for more intricate analyses. | **Column Name** | **Data Type** | **Description** | | ------------------ | ------------- | --------------------------------------------------------------------------- | | **product_name** | `string` | Standardized product name inferred across menu items (e.g. "chicken wings") | | **item_category** | `string` | Higher-level food or beverage category (e.g. "beer", "burger", "dessert") | | **product_sizing** | `string` | Parsed serving size (e.g. `330ml`, `12 pieces`, `1 bottle`, `Large`) | [Contact Us](https://dotlas.com) - **Stock-Keeping Item Attributes** ______________________________________________________________________ Information regarding an item's availability by judging if still in stock for purchase, or if specific discounts or promotions are available for items with a strikethrough price shown to customers, along with other metadata. | **Column Name** | **Data Type** | **Description** | | ------------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | | **is_available** | `boolean` | Whether or not the item is out of stock | | **is_popular** | `boolean` | Whether the item is marked as popular by some sources or if it appears in suggestive section names like "Most Trending" or "Best Selling" | | **item_price_discounted** | `string` | Available alongside `item_price` per measurement of restaurant menu to judge if certain items are available with an offer or promotion | [Contact Us](https://dotlas.com) ## Digitized Menu Dataset 🗃️ High-fidelity, structured menu data parsed directly from source PDFs using fine-tuned OCR vision & language models. This dataset provides a comprehensive, item-level view of restaurant menus, parsed directly from original menu PDFs. It bridges the gap between unstructured public menu information and actionable, structured data suitable for rigorous analysis. Useful for analyzing prices for: - Alcoholic beverages and drinks which are not often listed on delivery apps or online sites. - Restaurants with a primarily dine-in offering or dining establishments partnered with hospitality groups. - [Access on Databricks](https://marketplace.databricks.com/details/c2f25174-8b55-44ad-867b-8b1b2624bbfd/Dotlas_Menu-Intelligence-Dataset-from-Digitized-PDFs-Images) ## Menu Events (Changelog) Dataset [Contact Us](https://dotlas.com) If you're looking for a comprehensive changelog of menu events over time for a market, our Menu Events data product provides a real-time feed of menu modifications across restaurants. This specialized dataset tracks market movements in pricing, product availability, and menu composition on a high-frequency basis, maintaining a detailed record of all changes observed over time. The data stream can be: - Consumed via API endpoints for real-time monitoring - Integrated into notification systems to alert on specific changes - Used to track competitor pricing and product strategies - Monitor stock availability and inventory patterns - Analyze seasonal menu rotations and pricing trends This enables use cases from automated competitive intelligence to supply chain optimization. The feed format makes it easy to build applications that react to market changes as they happen. Example visualization below. # Restaurant Activations & Promotions Info This dataset is available for any market and is designed to complement [Restaurant Menus](https://catalog.dotlas.com/restaurants/menus/index.md) and [Unified Restaurant Establishments](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/index.md), enabling full-funnel restaurant performance tracking and market visibility. Dotlas maintains a real-time and historical dataset capturing promotional activations, offers, and brand campaigns across restaurants in global markets. These activations include platform-based discounts, event-driven promotions, limited-time offers, social media contests, and loyalty-focused campaigns. Every activation is linked to a verified outlet and brand, and is tagged by type, duration, source platform, and promotional strength, enabling clear, structured comparisons between outlets, brands, and neighborhoods. This dataset is especially useful for: - **Marketing and Brand Teams** looking to benchmark promotional intensity, detect activation gaps, and plan offer calendars. - **Pricing Teams** experimenting with discount structures (10%, 20%, 30%) and tracking category-level campaign frequency. - **Strategy Teams** comparing offer types used by competitors in nearby neighborhoods or by seasonality. - **Performance Analysts** studying how promotions affect menu price points, consumer engagement, or outlet sales over time. ## Use Cases for Marketing & Strategy Teams - **Experiment with discount ranges**: Monitor how often 10%, 20%, or 30% discounts are used by your brand or competitors and adjust promotional strategy accordingly. - **Local vs. Global Offer Penetration**: Track activations across neighborhoods to assess saturation, identify under-served areas, and plan targeted campaigns. - **Seasonal & Event-Based Trends**: Identify spikes in activation during holidays, paydays, or major events and align campaigns with high-response windows. - **Fine-tune offer formats**: Compare effectiveness of bundle deals vs. percentage discounts vs. influencer-led promos across locations. - **Benchmark against competitors**: Understand how frequently nearby or category-similar restaurants run offers, and adjust intensity to stay competitive. - **Automated alerting & dashboards**: Power dynamic dashboards or alerting systems to notify your teams when a competitor runs a high-discount campaign in your area. ## Data Dictionary - [Contact Us](https://dotlas.com) All offer and activation records follow a consistent schema, filterable by `restaurant_id`, `brand_id`, `date`, or `activation_type`. | **Column Name** | **Data Type** | **Description** | | -------------------------- | ------------- | ----------------------------------------------------------------------------------------- | | **restaurant_id** | `string` | Unique Dotlas-assigned identifier for the restaurant | | **brand_id** | `string` | Unique Dotlas-assigned identifier for the restaurant brand | | **activation_id** | `string` | Unique ID representing a single offer or campaign instance | | **activation_title** | `string` | Short title or callout of the offer (e.g. "20% Off Mains", "Win a Trip to Italy") | | **activation_description** | `string` | Long-form description of the offer as seen on the platform or post | | **activation_type** | `string` | Categorized type (e.g. `percentage_discount`, `free_delivery`, `bundle_offer`, `contest`) | | **discount_percent** | `float` | If applicable, the percentage discount offered (e.g. 10, 20, 30) | | **event_flag** | `boolean` | Indicates if this is a special event-based activation (e.g. holiday, contest) | | **source_platform** | `string` | Platform or origin of the promotion (e.g. Talabat, Instagram, Field Survey) | | **start_date** | `date` | Start date of the promotional activity | | **end_date** | `date` | End date of the promotional activity | | **created_at** | `timestamp` | Timestamp when this activation was first observed | | **neighbourhood_id** | `string` | Dotlas-assigned ID for the neighborhood where the restaurant is located | # Customer Reviews & Sentiment Dotlas aggregates customer reviews from diverse online sources including food delivery platforms, review aggregators, social channels, and brand pages. Each review is processed through advanced reasoning and natural language understanding models to generate: - **ABSA scores** (Aspect-Based Sentiment Analysis) for `food`, `service`, `ambiance`, `value`, and more - **NPS Sentiment Rating** (1-5 scale) aligned with Net Promoter Score methodology - **Categorized topics** automatically extracted from the text (e.g. `staff friendliness`, `food temperature`, `accessibility`) - **Time-series tracking** of review quality per topic or outlet This transforms qualitative customer voices into structured, quantifiable insights that can be compared across time, restaurants, brands, and topics. ## Use Cases for Marketing, CX & Product Teams - **Track dNPS movement** across time to detect brand health deterioration early - **Identify sentiment gaps**: strong food but weak service? Value declining? - **Pinpoint CX strengths or issues** at the brand, outlet, or city level using topic-tag trends - **Quantify qualitative feedback** into structured, actionable dashboards - **Benchmark seasonal shifts** in brand experience - **Use AI-tagged praise or complaints** in marketing, operations and menu R&D - **Evaluate campaigns**: do new activations lead to better NPS scores in target cities? ______________________________________________________________________ ## Data Dictionary: Individual Review Level - [Access on Databricks](https://marketplace.databricks.com/details/dde5767b-8ba2-4e39-9efc-7b371470bd41/Dotlas_Restaurants-and-FB-Dataset-United-States-of-America-US-Sample) - Access on Snowflake Each row represents one customer review as interpreted by the Dotlas AI pipeline. | **Column Name** | **Data Type** | **Description** | | ----------------------- | --------------- | ---------------------------------------------------------------------------------- | | **review_id** | `string` | Unique identifier for the individual review | | **restaurant_id** | `string` | Dotlas-assigned ID of the restaurant being reviewed | | **brand_id** | `string` | ID of the restaurant chain or brand | | **review_text** | `string` | Raw text of the original customer review | | **review_source** | `string` | Platform or website where the review was posted | | **reviewed_at** | `timestamp` | Date and time the review was posted | | **nps_sentiment_score** | `integer` | Customer sentiment on a 1-5 scale based on NPS reasoning | | **aspect_food** | `float` | ABSA score for the food (1.0 to 5.0) | | **aspect_service** | `float` | ABSA score for service (1.0 to 5.0) | | **aspect_ambiance** | `float` | ABSA score for ambiance/environment | | **aspect_value** | `float` | ABSA score for value for money | | **topic_tags** | `array[string]` | List of detected topics (e.g. `temperature`, `staff friendliness`, `portion size`) | | **language** | `string` | Language of the original review | | **neighbourhood_id** | `string` | Neighborhood ID of the restaurant reviewed | | **city** | `string` | City where the review was recorded | ______________________________________________________________________ ## Data Dictionary: Brand-Level dNPS Trends - [Access on Databricks](https://marketplace.databricks.com/details/dde5767b-8ba2-4e39-9efc-7b371470bd41/Dotlas_Restaurants-and-FB-Dataset-United-States-of-America-US-Sample) - Access on Snowflake Each row aggregates customer review sentiment at the brand and time level for performance tracking. | **Column Name** | **Data Type** | **Description** | | ------------------------ | --------------- | ---------------------------------------------------------------------------- | | **brand_id** | `string` | Dotlas-assigned brand identifier | | **date** | `date` | Daily or weekly aggregation date | | **city** | `string` | City where reviews were aggregated | | **dNPS_score** | `float` | Derived Net Promoter Score (-100 to +100), based on NPS bucket distributions | | **total_reviews** | `integer` | Count of reviews contributing to this aggregation | | **avg_aspect_food** | `float` | Average food rating score across reviews | | **avg_aspect_service** | `float` | Average service rating score | | **avg_aspect_ambiance** | `float` | Average ambiance score | | **avg_aspect_value** | `float` | Average value-for-money score | | **top_topics** | `array[string]` | Most frequently mentioned topics (e.g. `slow service`, `good quantity`) | | **negative_topic_count** | `integer` | Number of reviews associated with at least one negative topic | | **positive_topic_count** | `integer` | Number of reviews associated with only positive topics | ______________________________________________________________________ # Food-Delivery Statistics Dotlas maintains a high-frequency and near real-time measurement dataset that captures estimated delivery times and fees charged by restaurants on food delivery platforms across all observable neighborhoods in a city. For each restaurant, we simulate a customer located in various neighborhoods and collect: - **Estimated Time of Arrival (ETA)** - **Delivery Fee** charged by the platform - **Neighborhood** of the simulated customer location - **Time of Day** (breakfast, lunch, dinner slots) This dataset enables powerful analysis of delivery experience across: - Brands and cuisines - Time slots and service load - Neighborhoods and zones - Delivery aggregators and pricing strategies Unlike static attributes, these signals reflect dynamic platform conditions including batching, traffic, surge pricing, and operational efficiency. ### Use-Cases - Average delivery times per brand, cuisine, or zone - Fee competitiveness across platforms and locations - Identify underserved neighborhoods with high demand but slow delivery - Compare delivery performance across time of day - Benchmark competitor SLAs by area - Inform fulfillment strategy and ghost kitchen expansion - Coordinate promo timings to match fastest service windows Sample Dashboards or Analyses reflective of this data: ## Data Dictionary - [Access on Databricks](https://marketplace.databricks.com/details/cd983a72-9c29-49b9-8d0b-a24607d20512/Dotlas_Food-Delivery-Times-Fees-by-Area-OnTime-Performance-Data-Stream-Sample) Each record represents the delivery stats of a restaurant to a specific neighborhood at a given time of day. | **Column Name** | **Data Type** | **Description** | | ------------------------ | ------------- | ------------------------------------------------------------------------ | | **restaurant_id** | `string` | Unique Dotlas-assigned identifier for the restaurant | | **brand_id** | `string` | Dotlas-assigned brand/group the restaurant belongs to | | **neighbourhood_id** | `string` | Dotlas-assigned ID for the neighborhood receiving the delivery | | **city** | `string` | City in which the neighborhood and restaurant are located | | **delivery_eta_minutes** | `integer` | Estimated delivery time to this neighborhood (in minutes) | | **delivery_fee** | `float` | Delivery fee charged for the given route and time | | **currency** | `string` | Currency of the delivery fee | | **delivery_service** | `string` | Platform from which the ETA and fee were collected (e.g. Talabat, Jahez) | | **meal_period** | `string` | Time window when measurement occurred: `breakfast`, `lunch`, `dinner` | | **snapshot_at** | `timestamp` | Timestamp when the ETA and fee were recorded | ## Delivery Radius Data Product For spatial modeling of restaurant reach, Dotlas also offers a polygonal dataset that maps the **delivery footprint** of each restaurant by time of day and platform. The **Delivery Radius** dataset contains per-restaurant service areas that define where delivery is offered (and where it isn't), based on real-world ETA coverage. Each polygon includes: - `restaurant_id`, `meal_period`, `delivery_service`, and `geometry` - Areas where ETAs and fees are actually reported by the platform This allows visualization of: - Reach expansion or contraction by daypart - Delivery zone gaps and opportunities - Platform-specific coverage differences # Michelin Guide Restaurants This dataset is available across all global countries. Discover the world's most celebrated restaurants with our custom captured and curated Michelin Guide dataset, tracking the latest star-rated establishments across dozens of countries. This dataset provides structured, reliable information for culinary researchers, food industry professionals, and hospitality businesses to monitor trends and opportunities globally. - [Can a Michelin Star Actually Hurt Your Restaurant?](https://www.dotlas.com/blog/michelin-star-oct-2024) ______________________________________________________________________ See this dataset in action on an article exploring Michelin Guide trends and the impact of Michelin stars on restaurant performance. ## Data Dictionary - [Access on Databricks](https://marketplace.databricks.com/details/515dec72-df10-40fc-9595-9b7849b776ac/Dotlas_Michelin-Guide-Establishments) | **Column Name** | **Data Type** | **Description** | | ------------------------------- | --------------- | ------------------------------------------------------------------------------------ | | **michelinguide_restaurant_id** | `string` | The unique ID assigned to an outlet by the Michelin Guide | | **brand_name** | `string` | Official name of the restaurant as listed in the Michelin Guide | | **country** | `string` | Country where the restaurant is located | | **subregion** | `string` | State, province, or administrative region of the restaurant | | **city** | `string` | City where the restaurant is located | | **address** | `string` | Full street address of the restaurant | | **latitude** | `float` | Geographic latitude of the restaurant's location | | **longitude** | `float` | Geographic longitude of the restaurant's location | | **categories** | `array[string]` | Cuisines or classification tags assigned to the restaurant (e.g. `French`, `Modern`) | | **award** | `string` | Recognition given by the Michelin Guide (e.g. `1 Star`, `2 Stars`, `Bib Gourmand`) | | **currency** | `string` | Currency used in the pricing display for the restaurant | | **price_range** | `string` | General price level or category | | **restaurant_website** | `string` | Official website of the restaurant, if available | | **chef** | `string` | Name of the head chef or main culinary figure at the restaurant, if listed | | **captured_month** | `string` | Month when the data was last captured | | **published_date** | `date` | Date when the Michelin Guide listing was published | | **operating_hours_sun** | `string` | Restaurant operating hours for Sunday | | **operating_hours_mon** | `string` | Restaurant operating hours for Monday | | **operating_hours_tue** | `string` | Restaurant operating hours for Tuesday | | **operating_hours_wed** | `string` | Restaurant operating hours for Wednesday | | **operating_hours_thu** | `string` | Restaurant operating hours for Thursday | | **operating_hours_fri** | `string` | Restaurant operating hours for Friday | | **operating_hours_sat** | `string` | Restaurant operating hours for Saturday | | **url** | `string` | Original source URL of the Michelin Guide listing for the restaurant | # Hotels # Hotels - **Unified Hotel Establishments** ______________________________________________________________________ A comprehensive dataset consolidating hotel information from diverse online and offline sources, providing a unified view of each venue with detailed attributes. [View Data Products](https://catalog.dotlas.com/hotels/unified_hotel_establishments/index.md) - **Hotel Pricing Calendar** ______________________________________________________________________ Time-Series based pricing data for future hotel prices with continous monitoring. [View Data Products](https://catalog.dotlas.com/hotels/pricing/index.md) ______________________________________________________________________ # Hotel Pricing & Availability Dataset This data product is available for any market or country globally Dotlas provides a forward-looking hotel pricing dataset that captures room-level prices, availability, and booking conditions across a global network of hotel properties. The dataset offers daily and weekly snapshots of future rates for up to 365 days in advance, enabling precise pricing intelligence and demand forecasting at scale. Collected from OTA platforms and structured into a standardized schema, this dataset aligns with Dotlas’ [Unified Hotel Establishments](https://catalog.dotlas.com/hotels/unified_hotel_establishments/index.md) data, making it easy to link prices back to hotel attributes, amenities, and locations. Each record includes room type, stay date, quoted price, currency, booking constraints (e.g., minimum nights), and real-time availability, allowing flexible modeling across time, geography, and property tiers. ______________________________________________________________________ ## Potential Sectors for Use-Cases - Revenue Management & Dynamic Pricing - Market Rate Intelligence & Monitoring - Booking Horizon & Lead Time Analysis - Hotel Rate Calendars & Seasonality Forecasting - Competitor Rate Benchmarking - AI/ML Models for Demand Prediction - Site Feasibility & Rate Catchment Mapping - Integration with Travel Portals, Meta Search, or GIS Tools ______________________________________________________________________ This dataset is ideal for teams building travel tech products, hotel benchmarking platforms, real estate investment tools, or dynamic pricing engines, where temporal pricing signals are critical. ## Data Dictionary - [Access on Databricks](https://marketplace.databricks.com/details/f1cd048e-f54b-4c61-a3ed-0a656747470c/Dotlas_Unified-Hotels-Hospitality-Data-Pricing-Rooms-Amenities-Listings-More) | **Column Name** | **Data Type** | **Description** | | ---------------------- | ------------- | ------------------------------------------------------------------------------------------------- | | **country** | `string` | The country where the hotel is located. Matches with the corresponding field in hotel metadata. | | **check_in_date** | `date` | The calendar date for which this pricing entry is valid (i.e., the check-in date). | | **currency** | `string` | The ISO 4217 currency code in which the price is quoted (e.g., `JPY`, `USD`). | | **avg_price** | `float` | The average price per night (in the listed currency) for the specified date and room combination. | | **min_length_of_stay** | `integer` | Minimum number of nights required for booking at the given price. | | **is_available** | `boolean` | Indicates whether the room is available for booking on the specified check-in date. | | **is_vat_included** | `boolean` | Indicates whether the listed price includes VAT and applicable taxes. | # Unified Hotel Establishments This data product is available for any market or country globally Dotlas maintains a unified collection of hotel Points-of-Interest (PoIs) aggregated from multiple online and offline sources, linked together into a single-source-of-truth hospitality dataset. This dataset merges and enriches records from OTA platforms (like Booking.com, Expedia), official directories, brand websites, licensing registries, and proprietary field research using advanced entity resolution and deduplication techniques. Each hotel property is matched across sources using geospatial coordinates, name similarity, brand metadata, and contextual signals, resulting in a high-confidence, non-redundant view of physical lodging establishments and their associated details. The outcome is a ready-to-use collection of hotel profiles with standardized attributes including amenities, room types, pricing, guest policies, and star ratings. ______________________________________________________________________ ## Potential Sectors for Use-Cases - Hospitality & Lodging Analytics - Travel Tech & OTA Platforms - Location Intelligence & GIS - Urban Planning & Real Estate - Competitor Intelligence for Hotel Chains - Tourism Forecasting & Planning - Site Selection & Investment Feasibility - Brand Expansion & Franchise Tracking ______________________________________________________________________ This dataset supports both chain-level and individual property-level analysis, suitable for integration into geospatial platforms, pricing engines, review aggregators, or real estate models. ## Hotels Data Dictionary - [Access on Databricks](https://marketplace.databricks.com/details/f1cd048e-f54b-4c61-a3ed-0a656747470c/Dotlas_Unified-Hotels-Hospitality-Data-Pricing-Rooms-Amenities-Listings-More) | **Column Name** | **Data Type** | **Description** | | ------------------------ | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **hotel_id** | `string` | Unique Dotlas-assigned identifier for the hotel establishment. Useful for tracking the same physical venue over time. | | **hotel_name** | `string` | The name of the hotel venue | | **country** | `string` | The country in which the hotel is located | | **city** | `string` | The city where the hotel is located | | **subregion** | `string` | The state / district / territory in which the hotel is located. Example: `California`, `Normandy`, `Baden-Württemberg`, etc. | | **zipcode** | `string` | The postal code of the hotel, if available | | **area** | `string` | The Dotlas-assigned colloquial neighbourhood of the hotel, if available for our [supported urban boundaries](https://catalog.dotlas.com/maps/urban_boundaries/index.md). Example: `Downtown`, `Marina`, `Morningside Heights`, etc. | | **latitude** | `float` | The exact location coordinate of the hotel (with customizable projections) | | **longitude** | `float` | The exact location coordinate of the hotel (with customizable projections) | | **address** | `string` | The full address of the hotel, including street, city, state, and postal code | | **rating** | `float` | The average user rating of the hotel, split by source if required | | **star_rating** | `float` | The average star-based rating of the hotel, split by source if required | | **review_count** | `int` | The number of reviews for the hotel, split by source if required | | **amenities** | `string[]` | The amenities available at the hotel, if available. Example: `["Free Wi-Fi", "Swimming Pool", "Gym"]` | | **telephone_number** | `string` | The desk phone number of the hotel, if available | | **restaurants_in_venue** | `string[]` | The restaurants located within the hotel, if available. Example: `["The Grill", "The Bistro"]` | | **policies_and_rules** | `string` | The policies and rules of the hotel, if available. Example: `["Pet-friendly", "Kids age 0-12 stay free", "No parties or groups allowed"]` | | **cancellation_policy** | `string` | The cancellation policy of the hotel, if available. Example: `["Free cancellation until 24 hours before arrival", "Non-refundable"]` | | **room_types** | `string[]` | The types of rooms available at the hotel, if available. Example: `["Studio", "Double Room", "Suite"]` | | **photos** | `string[]` | The URLs of photos of the hotel, if available. Additional metadata like date of capture or type of photo available on request. | We can also provide additional fields on request, such as parent company of hotel chain, website, continous monitoring of hotel attributes, and more. Please reach out to us for more information. # Maps # Maps - **Urban Boundaries** ______________________________________________________________________ Our custom curated urban boundaries for neighbourhoods and localities. [View Data Products](https://catalog.dotlas.com/maps/urban_boundaries/index.md) - **Zipcodes** ______________________________________________________________________ Postal or Zipcode boundaries for select geographies [View Data Products](https://catalog.dotlas.com/maps/zipcodes/index.md) ______________________________________________________________________ # Urban Boundaries **This data product is available through an annual data license** ## About Dotlas maintains a comprehensive collection of meticulously self-curated urban boundary data across global markets. These boundaries define distinct neighborhoods and localities as precise polygon geometries, reflecting both official administrative divisions and locally recognized areas. Urban boundaries can form the bedrock of spatial studies, providing the flexibility to run complex spatial joins to match locations, roads, markers, and other geospatial datasets aggregated by neighborhoods for enhanced insights. Our boundary data is: - Created through extensive collaboration with GIS experts and local specialists - Updated either annually or semi-annually to ensure accuracy - Available for custom market expansion upon request This dataset is particularly valuable for the following use-cases: - Market analysis and planning - Service area optimization - Location-based applications & Demographic studies Each boundary is carefully validated to ensure it accurately represents real-world geographic divisions while accounting for local context and usage. All vector-based geospatial formats are supported, so whether you're using cloud-native geospatial, or just want to load up the boundaries into ArcGIS or QGIS on your desktop, our boundaries have you covered. ## Data Dictionary All urban boundaries have the same schema, filterable by `country`, `city` or the `name` of the neighbourhood. | **Column Name** | **Data Type** | **Description** | | ---------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------ | | **name** | `string` | Name of the neighbourhood or boundary | | **city** | `string` | City in which neighbourhood is located | | **geometry** | `string` (geometry) | Full geometry of the shape ([Polygon in WKT format](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry)) | | **country** | `string` | Country in which the neighbourhood and city are located | | **neighbourhood_id** | `string` | Dotlas-assigned unique identifier for the associated neighbourhood across geographies | | **centroid_longitude** | `float` | Longitude value of the centroid location of the neighbourhood | | **centroid_latitude** | `float` | Latitude value of the centroid location of the neighbourhood | ## Products Don't see a listed country for which you require urban boundaries? We're always expanding our library of off-the-shelf GIS products, but we might still be able to provision efforts to gather the necessary data and create a custom curation for your needs. Contact us for more details. ### 🇦🇪 United Arab Emirates - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons including popular locations like Business Bay, Al Karama, JLT in Dubai. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons representing all major cities in the United Arab Emirates. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Dubai, Sharjah, Ajman and Umm Al Quwain cities in the UAE. ______________________________________________________________________ ### 🇰🇼 Kuwait - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons including prominent areas like Salmiya, Hawally, and Kuwait City's financial district. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons covering Kuwait's major urban areas including Kuwait City, Jahra, and Ahmadi. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Kuwait. ______________________________________________________________________ ### 🇶🇦 Qatar - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons including The Pearl, West Bay, Lusail, and other prominent Doha districts. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons encompassing Doha, Al Wakrah, Al Khor, and other major Qatari municipalities. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Qatar (Doha / Lusail). ______________________________________________________________________ ### 🇧🇭 Bahrain - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons covering Seef, Juffair, Manama's Diplomatic Area, and other key districts. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons including Manama, Riffa, Muharraq, and other major urban centers. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Bahrain (Manama and other Governorates). ______________________________________________________________________ ### 🇪🇬 Egypt - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons including Zamalek, Maadi, Nasr City, and other vital Cairo districts. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons covering Cairo, Alexandria, Giza, and other major Egyptian cities. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Cairo and parts of Giza. ______________________________________________________________________ ### 🇴🇲 Oman - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons including Qurum, Ruwi, Muttrah, and other key areas of Muscat. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons covering Muscat, Salalah, Sohar, and other major Omani cities. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Muscat and parts of Seeb. ______________________________________________________________________ ### 🇯🇴 Jordan - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons including Abdoun, Sweifieh, Jabal Amman, and other prime Amman districts. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons includes Amman only. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Amman. ______________________________________________________________________ ### 🇸🇦 Saudi Arabia - **Neighbourhoods** ______________________________________________________________________ Neighbourhood polygons including Al Olaya, Al Malaz, Al Muhammadiyah, and other key districts in major cities. [Contact Us](https://dotlas.com) - **Cities** ______________________________________________________________________ City-wide polygons covering Riyadh, Jeddah, Dammam, and other major Saudi cities. [Contact Us](https://dotlas.com) View Example Boundaries on the Map Map indicates neighbourhood-level boundaries for Jeddah. ______________________________________________________________________ # Zipcodes **This data product is available for free access and usage.** ## About Dotlas maintains a comprehensive collection of ZIP code boundary data across global markets. These boundaries define postal code areas as precise polygon geometries, reflecting official administrative postal divisions. The zip code data we collect is: - Sourced from official postal authorities - Updated regularly to reflect postal code changes All standard geospatial formats are supported for easy integration with your preferred mapping tools. ## Data Dictionary | **Column Name** | **Data Type** | **Description** | | --------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | | **zipcode** | `string` | Zipcode value | | **subregion** | `string` | State / Territory of the country in which zipcode is located | | **geometry** | `string` (geometry) | Full geometry of the zipcode shape ([Polygon in WKT format](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry)) | | **country** | `string` | Country in which the zipcode is located | ## Products ### 🇺🇸 United States Zipcode Boundaries - **US Zipcode Boundaries** ______________________________________________________________________ Zipcode boundaries and zipcode values for the United States including non-continental territories. [Access on Databricks](https://marketplace.databricks.com/details/70bcb62a-e53a-4ccc-969d-3152a6524fde/Dotlas_United-States-Zipcode-Boundaries-with-Shapefiles-GeoJSON-US-Zips) View Example Boundaries on the Map ### 🇨🇦 Canada Level-3 Zipcode Boundaries - **Canada Level-3 Zipcode Boundaries** ______________________________________________________________________ Level-3 Zipcode boundaries and zipcode values for Canada Access on Databricks View Example Boundaries on the Map Toronto and nearby surroundings available zipcode boundaries # Getting started # Home > Supported data marketplaces ## Premium Industry Leading and Publicly Sourced Market Data **Power your systems and decisions with trusted, high-quality industry data** We offer ready-to-use datasets built from millions of data points sourced online and offline. Our advanced Data & AI pipelines clean, enrich, and unify this information in-house, delivering contextual, reliable datasets tailored for real-world use in your industry. Each dataset undergoes rigorous quality checks and standardization processes to ensure accuracy and consistency. With regular updates and historical data preservation, you'll have access to both current market insights and trending patterns over time. Whether you're performing market analysis, competitive intelligence, or building machine learning models, our structured data formats and detailed documentation make integration seamless into your existing workflows. Looking for Insights, not Data? Our fully managed SaaS application for retail market intelligence may be of greater interest if you don't want to sift through the data or have a dedicated data science team on the ground. Visit [dotlas.com](https://dotlas.com) to learn more. ## Catalog View data products by industry, and find more information and coverage for each in the respective pages. - **Restaurants** ______________________________________________________________________ Access a wealth of restaurant dimensional data including locations, operating hours, reviews, menus, pricing, promotions, advertisements, social media presence and much more. [Browse Restaurants](https://catalog.dotlas.com/restaurants/index.md) - **Hotels & Hospitality** ______________________________________________________________________ Stay on top of competitive hotel pricing, availability and accommodations, together with nearby urban events that affect guest experiences. [Browse Hotels](https://catalog.dotlas.com/hotels/index.md) - **Maps & Geospatial** ______________________________________________________________________ Sift through a collection of curated GIS data to better aggregate and transform location information to actionable insights. [Browse Maps](https://catalog.dotlas.com/maps/index.md) # Access & Support ## Access Models We offer flexible methods to gain access and utilize industry data. We strictly believe in the power data brings to the table for driving decisions, and work with our customers to ensure they can derive value from purchased datasets. - **Subscription Access** Continuous data synchronization with regular updates and historical coverage maintenance. - **One-off Downloads** Point-in-time snapshots of data for specific use cases or analysis. - **Enterprise Licensing** Custom coverage, redistribution rights, and specialized support for enterprise needs. - **Integration & Enrichment** Our team works with you to seamlessly conflate market data into your data infrastructure to enhance business insights. ## Formats Our datasets are available in multiple industry-standard formats for easy integration. Each format is optimized for performance and compatibility with common data analysis tools. - **CSVs and Excel Files** Delimited flat-files for widespread access and spreadsheet use. Consumable by both the business and tech teams. - **JSON Data** Structured documents to consume for developers and LLMs alike. - **Parquet** High-performance format for data lakes, warehouses, and engineering pipelines. ## Destinations We serve up our data products on a variety of cloud destinations or even local storage options based on consumer preference. ### Cloud Sharing - **Workspace Drive Folders** Either GoogleDrive, OneDrive, DropBox or more, sync up with those familiar destinations for non-technical access. - **Amazon (AWS) S3 Storage** Access files directly over AWS S3 buckets or pre-signed URLs. - **Azure Blob Storage** Microsoft Azure blob integration with access across Microsoft products. - **Google Cloud Storage (GCS)** Accessible for teams hosting their cloud resources on GCP. - **Databricks Delta Sharing** Share across clouds and workspaces. - **Snowflake Data Cloud** Query-ready data, directly in your warehouse. ### Integration Offerings - **BI Dashboards** Plug our data into Power BI, Tableau, or your preferred dashboard tools via connectors - **API Access** REST API-based metered data consumption available **on request**. - **AI Agent Access (MCP)** Connect an AI assistant to Dotlas data through the Model Context Protocol and ask questions in plain language. See [AI Access (MCPs)](https://catalog.dotlas.com/ai_access/index.md). ## Frequently asked questions (FAQs) Where can I see a sample of data? The Dotlas catalog offers documentation of datasets that we list online. To view the actual datasets samples, you may visit any one of the following: - [Dotlas Samples Page](https://dotlas.com/data-restaurant) - [Databricks Marketplace](https://marketplace.databricks.com/?provider=Dotlas) - Snowflake Data Cloud Note that the latter two options require a Databricks and Snowflake account for your organization respectively. I can't find data samples for my specific location While we may not display samples for every geographic location, our data format remains consistent across different regions. For example: - Restaurant data follows the same structure whether it's from New York or Tokyo - Menu data maintains similar fields across different countries If you're interested in a specific region not shown in our samples, please contact us. We can likely source the data for your target location. How fresh or recent is the data? Our data refresh rates vary by product type and source: - **Real-time Data**: Some datasets are updated nearly continuously, providing current market snapshots - **Regular Updates**: Other datasets refresh daily, weekly, or monthly The update frequency depends on: - Data point volatility - Analysis requirements - Source availability Contact us for specific refresh rates of datasets you're interested in. Do you offer historical data snapshots? Yes, we maintain historical data snapshots for most of our supported data products. - **Time Range**: Historical data availability varies by dataset and region - **Frequency**: Snapshots are typically stored at regular intervals - **Coverage**: Historical depth depends on when we began collecting data for that specific dataset Contact us with your specific requirements for: - Exact time ranges available - Snapshot granularity - Historical data pricing and we can advise on what's available. Can you combine datasets or enrich them with my own data? Yes. We regularly help customers: - Join our datasets with CRM or internal data based on a mutual NDA (Non-disclosure agreement) and accepted privacy policies. - Enrich customer data with geographic or demographic context. - Normalize and clean external menu data for cross-market comparisons Just send us a sample and we’ll tell you what’s possible. What are the data usage terms? You can read more on our [privacy policy](https://www.dotlas.com/privacy-policy) and [terms of service](https://www.dotlas.com/terms-of-service). Purchase of data products can be one-offs or subscription based periodic deliveries. Some data maybe subject to a data licensure, feel free to ask us more about it. Pro-bono data access for academic purposes? - Unfortunately, we currently cannot share industry data towards free or subsidized academic / educational pursuits. - We do make some geographical datasets fully and publicly available, so you're free to directly consume those. - **If you're an institution that is looking to license data for non-academic or non-tutelage purposes, feel free to contact us**.