How to Scrape Google Maps Data in 2026

Why Scrape Google Maps?

Google Maps contains one of the richest datasets of local business information available on the web. From restaurants and real estate agents to dentists and car dealerships — the data is there, structured and waiting to be collected.

Whether you're building a lead generation pipeline, doing competitor research, or enriching your CRM, Google Maps data is invaluable. But extracting it at scale requires the right approach.

Pro Tip

PullData handles anti-bot detection, proxy rotation, and CAPTCHA solving automatically — so you can focus on what matters: the data.

Setting Up Your Google Maps Scraper

The fastest way to start is with PullData's pre-built Google Maps scraper module. Here's how the typical workflow looks:

Select the Google Maps Scraper from the catalog
Enter your search keywords (e.g., "dentists in Chicago")
Set your geographic parameters
Launch the job and export results

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scraper-config.json

json

{
  "scraper": "google-maps",
  "keywords": ["dentists in Chicago", "plumbers in Austin"],
  "fields": ["name", "address", "phone", "rating", "reviews"],
  "maxResults": 500,
  "exportFormat": "csv"
}

Handling Anti-Bot Detection

Google uses sophisticated bot detection. Here are the key techniques PullData uses under the hood:

Browser fingerprinting — Mimics real browser environments with proper headers, canvas fingerprints, and WebGL data
Smart proxy rotation — Rotates through residential proxies to avoid IP-based blocks
Request throttling — Intelligent rate limiting that adapts to Google's response patterns
CAPTCHA solving — Automated CAPTCHA bypass when triggered

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Structuring Your Output

Once the scraper finishes, you get clean CSV or JSON output with fields like:

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types.ts

typescript

interface GoogleMapsResult {
  name: string
  address: string
  phone: string | null
  website: string | null
  rating: number
  totalReviews: number
  category: string
  latitude: number
  longitude: number
  placeId: string
}