How to Scrape 1000s of Restaurant Data from Google Maps (2026 Guide)

Google Maps is no longer just a navigation tool. In 2026, it has become one of the world’s largest local business databases — with over 200 million businesses indexed across 220+ countries and 2 billion monthly active users searching for restaurants, cafes, food trucks, ghost kitchens, and grocery stores every single day.For sales teams, market researchers, food-tech developers, and FMCG brands, that database is a goldmine. Restaurant names, addresses, phone numbers, websites, ratings, reviews, opening hours, menu links, photo counts, and category tags — all publicly available, all structured, and all extractable at scale.The problem is volume. Search ‘pizza restaurants in California’ on Google Maps and you get over 1,000 results across dozens of pages. Search ‘restaurants in Dallas-Fort Worth’ and the number climbs to tens of thousands. Copying that manually into an Excel sheet row by row — name, address, phone, rating, review count, website — is a full working week for one person, and the data is already stale by the time you finish.This guide covers every method for scraping Google Maps restaurant data in 2026 — from Python scripts to managed services — with a clear breakdown of when each approach makes sense, what data you can extract, and how to export the results to CSV or Excel without writing code.

→ Skip straight to the data? Get a free 100-row sample of Google Maps restaurant data in 4 hours.

FoodSpark delivers business name, address, phone, rating, review count, hours, and menu URL — clean, structured, ready to use.

What Restaurant Data Can You Scrape from Google Maps?

Every Google Maps business listing exposes a consistent set of publicly visible fields. Here is what is extractable for restaurant listings specifically:

Business name Full restaurant name as listed	Address Street, city, state, zip code	Phone number Primary contact number
Website URL Linked business website	Google Maps URL Direct listing link	Aggregate rating Star rating (1.0–5.0)
Review count Total number of reviews	Opening hours Daily schedule + holiday hours	Price range $ to $$$$ classification
Business category e.g. Pizza, Sushi, Burger joint	Neighbourhood / area Sub-city location tag	Latitude / Longitude Geo-coordinates
Google Place ID Unique identifier for each listing	Review text Full customer review content	Review date Date of each review
Reviewer name Display name of reviewer	Owner response Business reply to reviews	Photo count Number of listing photos
Popular times Hourly busyness data	Menu link URL to menu page if listed	Business status Open / Closed / Permanently closed
Claimed / Unclaimed Whether listing is owner-verified

Custom fields and enrichment — including email address discovery, social media profile links, and owner contact details — are available via FoodSpark’s managed extraction service on request.

Why Businesses Scrape Google Maps Restaurant Data in 2026

Google Maps scraping demand is accelerating in 2026 across sales, marketing, market research, and food-tech use cases. Here are the most common workflows:

B2B Lead Generation Sales teams scrape restaurant listings by city and category to build targeted outreach lists — business name, phone number, website, and rating. A filtered list of pizza restaurants in DFW with 3.5–4.2 stars, a website, and no social ads pixel is a warm B2B prospect list most agencies would pay for.	Restaurant Location Data for Apps Food-tech developers use Google Maps data to seed restaurant databases for ordering apps, reservation platforms, and food discovery tools — covering name, address, hours, cuisine, price range, and geo-coordinates across any city or region.
Competitor Analysis Restaurant chains and food businesses scrape competitor listings to monitor ratings, review velocity, peak hours, and customer sentiment shifts — tracking which competitors are gaining momentum and which are losing it before it shows up in revenue.	Market Research & Expansion Planning FMCG brands, investment firms, and management consultancies use restaurant density data, cuisine category distribution, and rating benchmarks by metro area to identify market saturation, expansion opportunities, and underserved locations.
Export Google Maps to Excel / CSV The single most searched use case — teams want to download restaurant search results from Google Maps into a structured Excel or CSV file for internal databases, CRM imports, or client deliverables, without manually copying row by row.	Review Sentiment & Ratings Analysis AI and analytics teams scrape Google restaurant reviews to build sentiment models, track brand perception over time, and identify recurring customer complaints or praise patterns across a cuisine category or city.

5 Methods to Scrape Google Maps Restaurant Data in 2026

Here is an honest comparison of every available approach — with the tradeoffs that most guides leave out:

Method 1 — Manual Export (for very small projects only)

Google Maps does not provide a native ‘export to CSV’ button for search results. Manual extraction means copying each listing’s name, address, phone number, and rating by hand into an Excel sheet.Realistic output: 20–50 listings per hour, per person. For a list of 500 pizza restaurants in California — a common starting point for a DFW or California market research project — that is 10–25 hours of repetitive data entry before you have anything usable.When it makes sense: one-time projects under 50 listings where you need the data within the hour and have no budget for tooling.When it doesn’t: any project over 100 listings, any recurring data need, or any requirement for reviews data.

Method 2 — Google Maps API (for developers with API budget)

Google’s official Places API (now part of Google Maps Platform) gives structured access to business listing data programmatically — business name, address, phone number, rating, hours, and place ID — via REST API calls.Key limitations to understand before committing:

Review access is restricted — Google’s Places API returns only a small subset of reviews per listing, not the complete history
Cost per request adds up fast — at scale, Google Maps API costs can exceed $5,000/month for a restaurant database covering major US metros
Rate limits and quota caps restrict how much data you can pull in a given time window
No bulk export — you call the API per listing, not per city or category

Best use case: developers building apps that need real-time business details for a specific place_id, not bulk datasets of thousands of restaurants.

Method 3 — Python Scraper (for developers testing at small scale)

A Python script using Playwright, Selenium, or Requests + BeautifulSoup can extract Google Maps restaurant listings programmatically. The typical workflow:

Search Google Maps for your target category and location (e.g. ‘restaurants in Chicago’)
Scroll the sidebar to load all listings (Google Maps lazy-loads results)
Click each listing and extract structured fields from the detail panel
Export the collected data to CSV or JSON

What makes this harder than it sounds: Google Maps detects and blocks automated browsers using JavaScript fingerprinting, behavioural pattern analysis, and IP-based rate limiting. Scripts that work on day one often stop returning data within 48–72 hours as Google’s detection catches the pattern. Maintaining a Python scraper for ongoing restaurant data collection requires constant updates — proxy rotation, browser fingerprint randomisation, and logic updates every time Google changes its front-end structure.Best use case: developers running a one-time proof-of-concept extract of 500–2,000 restaurants. Not suitable for ongoing data needs or large-scale projects.

Method 4 — Self-Serve Scraping Tools (Apify, Outscraper, GMapsExtractor)

Self-serve Google Maps scraping tools let non-developers run extraction jobs through a web interface — input your search query, set your location, run the job, download the results as CSV or Excel. No code required.Popular options in 2026:

Apify Google Maps Scraper — 907+ reviews, 4.7 average. Caps at 1,000 places per run on standard plans. Per-credit pricing. Requires multiple runs and manual stitching for large datasets.
Outscraper — Specialises in Google Maps data extraction. Offers email enrichment as an add-on. Per-record pricing model. Good for lead lists in the 500–10,000 range.
GMapsExtractor — Free tier with 1,000 credits per month. Simple interface. Limited field coverage vs. managed services.

Tradeoffs: self-serve tools are fast for small to medium projects but become expensive at scale (per-credit pricing adds up at 50,000+ records), offer limited support for custom field sets, and require your team to manage the job scheduling and data assembly yourself.Best use case: SMBs and agencies with technical staff who need 1,000–20,000 restaurant records on a one-time or infrequent basis.

Method 5 — Managed Scraping Service (FoodSpark) — for scale, clean data, and recurring needs

FoodSpark’s managed Google Maps restaurant data extraction service handles everything — geographic targeting, anti-bot management, JavaScript rendering, data cleaning, deduplication, and scheduled delivery — with no volume cap, no per-credit pricing, and no scraper maintenance on your side.

How it works:

Tell us your target city, metro area, cuisine category, or restaurant list (e.g. ‘all pizza restaurants in DFW’, ‘Italian restaurants in NYC rated 4+ stars’, ‘all QSR chains in California’)
We scope the project, confirm field coverage, and deliver a free 100-row sample within 4 business hours — so you verify data quality before committing
Full dataset is delivered in CSV, JSON, Excel, or directly into BigQuery, Snowflake, or AWS S3 — within 24–48 hours for standard projects
For recurring needs (weekly refresh, monthly updates, new-listing monitoring), we set up automated delivery on your schedule

Best use case: businesses that need 10,000+ restaurant records, recurring data refresh, clean structured output, or a combination of Google Maps data with other platform data (Yelp, DoorDash, Uber Eats, Zomato) in a unified schema.

Google Maps Restaurant Data Extraction — Method Comparison

Method	Speed	Scale	Cost	Maintenance	Best for
Manual copy-paste	Very slow	100 listings max	Free (your time)	None	One-time tiny projects
Google Maps API	Fast	Limited by quota	Per-request fees	Medium	Developers with API budget
DIY Python scraper	Medium	Moderate	Low upfront, high ongoing	High — breaks constantly	Developers testing concepts
Self-serve tool (Apify, Outscraper)	Fast	1,000–10,000/run	Per-credit cost	Low	SMBs with technical staff
FoodSpark managed service	Fast	Unlimited	Predictable monthly	Zero — fully managed	Businesses needing scale + clean data

What You Can Build with Google Maps Restaurant Data

Structured restaurant data from Google Maps is the foundation for a wide range of business and product workflows:

B2B Sales Lead Lists Export restaurant listings by city, cuisine, rating range, and business size into a CRM-ready CSV. A filtered list of 2,000 pizza restaurants in DFW with 3.5–4.2 stars and a website is a targeted sales list most agencies use as the starting point for outreach campaigns.	Restaurant Location Database Seed a comprehensive restaurant directory app or food-ordering platform with name, address, geo-coordinates, opening hours, cuisine type, and price range — covering every restaurant in a city without manual data entry.
Google Restaurant Reviews API (Alternative) For teams that need full review history beyond what Google’s official API exposes, FoodSpark’s managed extraction delivers complete review text, reviewer details, star ratings, and owner responses for any restaurant listing — with no 3-review cap.	Market Expansion Research Map restaurant density, cuisine category distribution, and average rating benchmarks by neighbourhood, zip code, or metro area — the data foundation for franchise expansion decisions, investment due diligence, and competitive positioning reports.
Export Google Maps to Excel The most common request — a structured Excel or CSV file of all restaurants matching your search criteria, with name, address, phone, rating, review count, and category columns pre-formatted for CRM import or client delivery.	Sentiment & Review Analysis Feed Google Maps review data into sentiment analysis pipelines to track brand perception trends, identify recurring complaints in a cuisine category, and benchmark your restaurant’s reputation against competitors in the same area.

Exportar Restaurantes de Google Maps — Coverage for International Markets

A significant portion of Google Maps restaurant data requests come from Spanish-speaking markets — teams looking to export local business listings, restaurant directories, and supplier data from Google Maps across Latin America and Spain.FoodSpark‘s restaurant data scraping service covers Google Maps extraction in every country and language — including:

Exportar restaurantes de Google Maps — restaurant listings from any city in Mexico, Spain, Argentina, Colombia, or any other Spanish-speaking market
Exportar tiendas locales / negocios locales de Google Maps — local business and store directories by city, neighbourhood, or category
Exportar proveedores de Google Maps — supplier and vendor listings by industry and location
Exportar pizzerías / cientos / miles de negocios — bulk export of pizza restaurants or any business category at volume

Data is delivered in the same structured CSV or JSON format regardless of country — with localised field values (business names, addresses, and categories in the local language) and geo-coordinates for every listing.

How to Export Google Maps Restaurant Search Results to Excel

The most searched query on this topic — ‘export google map search results to excel’ — reflects a genuine pain point: Google Maps does not provide a built-in export button for business search results. Here are your options:

Option 1 — Self-serve tool: Use Outscraper or Apify’s Google Maps scraper. Input your search query (e.g. ‘restaurants in Dallas, TX’), run the job, and download the result as an Excel file. Free tiers available for small volumes. Best for one-time exports under 5,000 listings.
Option 2 — Google Maps API + Python: Use the Places API to pull listings by search query, collect results into a Pandas dataframe, and export to Excel with df.to_excel(). Requires a Google Cloud account and API key. Best for developers who need the export integrated into an existing workflow.
Option 3 — FoodSpark managed service: Tell us your search criteria (city, cuisine, rating range, any filters) and we deliver a clean, structured Excel or CSV file within 24 hours. No code, no API keys, no credit management. Best for business teams that need the data without touching any tooling.

For any Google Maps restaurant export above 10,000 records — covering full metro areas, multi-city comparisons, or ongoing refresh cycles — managed extraction is the only practical route. Self-serve tools require multiple manual runs, result stitching, and deduplication work that adds hours to every update cycle.

FAQs — Scraping Google Maps Restaurant Data

Is it legal to scrape Google Maps restaurant data?

Scraping publicly visible business listing data from Google Maps is generally lawful in most jurisdictions. The landmark hiQ Labs v. LinkedIn ruling affirmed that scraping publicly accessible web data does not violate the Computer Fraud and Abuse Act. FoodSpark extracts only publicly available data — business names, addresses, phone numbers, ratings, and reviews visible to any standard user without logging in. Google’s Terms of Service restrict automated access, so we recommend clients conduct their own legal review for their specific use case and jurisdiction.

What is the difference between the Google Maps API and scraping?

The Google Maps API (Places API) is Google’s official developer product — it gives structured access to business data via REST API calls, with per-request pricing and rate limits. Scraping extracts the same publicly visible data directly from the Maps interface, typically delivering more complete data (full review histories, popular times, photo counts) at lower cost per record. The API is best for real-time lookups of specific places; scraping is best for bulk datasets of thousands of restaurants across a city or region.

Can I get Google Maps restaurant data including reviews?

Yes. FoodSpark extracts full review data — review text, star rating, reviewer name, review date, and owner response — alongside business listing data. Google’s official Places API restricts review access to a small number of reviews per listing; managed scraping removes that limitation and delivers complete review histories for any restaurant you need monitored.

How do I scrape all restaurants in a specific area like DFW or California?

FoodSpark scopes extractions by city, metro area, state, zip code, or custom geographic boundary. For a full restaurant dataset covering Dallas-Fort Worth, California, or any other US metro, tell us your target area, cuisine filters, and rating threshold — we’ll confirm coverage, deliver a free 100-row sample, and scope the full extraction within 24 hours.

Can I get the data as a CSV or Excel file?

Yes. All FoodSpark deliverables are available in CSV, Excel (XLSX), JSON, or Parquet. For pipeline integration, we also deliver directly into AWS S3, Google BigQuery, Snowflake, or Google Sheets. Every file includes pre-formatted column headers matching standard CRM import templates.