Google Maps Data API: Extract Structured JSON in 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Use AlterLab's Extract API to get typed JSON from Google Maps pages. Provide a URL and a JSON schema describing the fields you need (business_name, rating, address, etc.). The API returns validated data without any HTML parsing.

Why use Google Maps data?

Publicly listed local data powers many applications:

• Training machine learning models for location‑based recommendations
• Building competitive intelligence dashboards that monitor price or hours changes
• Enriching CRM records with up‑to‑date business contact information

What data can you extract?

From a Google Maps place page you can pull these common fields:

• business_name – the official name shown on the listing
• rating – average star rating as a string (e.g., "4.7")
• address – full street address
• phone – primary contact number
• hours – opening hours formatted as a string
• category – business type such as "restaurant" or "gas station"

All of these are publicly visible; no login or private data is accessed.

The extraction approach

Scraping Google Maps with raw HTTP and HTML parsing is fragile. The page uses dynamic rendering, frequent class name changes, and anti‑bot measures. A data API that handles rendering, proxy rotation, and AI‑driven extraction removes that complexity. You define what you want via a schema; the API handles the how.

Quick start with AlterLab Extract API

First, install the AlterLab Python client (see the Getting started guide for setup). Then define a schema and call the extract endpoint.

```python title="extract_google-com-maps.py" {5-12}

client = alterlab.Client("YOUR_API_KEY")

schema = {
"type": "object",
"properties": {
"business_name": {
"type": "string",
"description": "The business name field"
},
"rating": {
"type": "string",
"description": "The rating field"
},
"address": {
"type": "string",
"description": "The address field"
},
"phone": {
"type": "string",
"description": "The phone field"
},
"hours": {
"type": "string",
"description": "The hours field"
},
"category": {
"type": "string",
"description": "The category field"
}
}
}

result = client.extract(
url="https://google.com/maps/search/coffee+shops+near+San+Francisco",
schema=schema,
)
print(result.data)

The same request works with cURL:



```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://google.com/maps/search/coffee+shops+near+San+Francisco",
    "schema": {
      "properties": {
        "business_name": {"type": "string"},
        "rating": {"type": "string"},
        "address": {"type": "string"},
        "phone": {"type": "string"},
        "hours": {"type": "string"},
        "category": {"type": "string"}
      }
    }
  }'

Both examples return a JSON object matching the schema, ready for downstream processing.

Example output

{
  "business_name": "Blue Bottle Coffee",
  "rating": "4.5",
  "address": "300 Webster St, Oakland, CA 94607",
  "phone": "+1 510-832-6200",
  "hours": "Mon–Sun: 6:00 AM–6:00 PM",
  "category": "Coffee shop"
}

Define your schema

The schema parameter drives the extraction. AlterLab validates each field against the type you specify and coerces values when possible (e.g., turning "4.5★" into "4.5"). If a field cannot be found, the API returns null for that property. This contract lets you treat the output as a reliable data source without writing custom parsers.

Handle pagination and scale

Google Maps results often span multiple pages. For high‑volume workflows:

• Batching: send an array of URLs in a single request (up to 100 per call)
• Async jobs: use the /jobs endpoint to process thousands of pages and retrieve results via a webhook or polling
• Rate limits: stay within your plan’s concurrency; see AlterLab pricing for tier details

A simple batch example in Python:

```python title="batch_extract.py" {5-10}

client = alterlab.Client("YOUR_API_KEY")

urls = [
"https://google.com/maps/search/pizza+New+York",
"https://google.com/maps/search/pizza+Los+Angeles",
"https://google.com/maps/search/pizza+Chicago"
]

schema = {
"type": "object",
"properties": {
"business_name": {"type": "string"},
"rating": {"type": "string"},
"address": {"type": "string"}
}
}

jobs = client.create_batch_job(urls=urls, schema=schema)
print("Batch submitted:", jobs.id)

Results arrive as typed JSON objects, eliminating the need for post‑processing cleanup.

## Key takeaways
- Define a clear JSON schema to get exactly the fields you need
- AlterLab’s Extract API handles rendering, proxies, and AI extraction so you receive ready‑to‑use data
- Use batching or async jobs for large scale; consult pricing for cost estimates
- Always verify that your target pages are publicly accessible and compliant with robots.txt and ToS

<div data-infographic="stats">
  <div data-stat data-value="99.2%" data-label="Extraction Accuracy"></div>
  <div data-stat data-value="1.4s" data-label="Avg Response Time"></div>
  <div data-stat data-value="100%" data-label="Typed JSON Output"></div>
</div>

<div data-infographic="steps">
  <div data-step data-number="1" data-title="Define Schema" data-description="Specify the fields you want as a JSON schema"></div>
  <div data-step data-number="2" data-title="Call Extract API" data-description="POST the URL + schema to AlterLab"></div>
  <div data-step data-number="3" data-title="Receive Typed JSON" data-description="Get back validated, structured data — no parsing needed"></div>
</div>

<div data-infographic="try-it" data-url="https://google.com/maps" data-description="Extract structured local data from Google Maps"></div>