Free Google Maps Data Scraper for Leads

Unlocking access to real-time business data for lead generation and market research is a challenge for many marketers, founders, and growth hackers. Google Maps data scraping has become a popular tactic, enabling users to collect detailed business information quickly and efficiently. In this comprehensive guide, we’ll dive deep into a hands-on walk-through of a new free SaaS tool—“scrape table”—exploring how it works, how it was built with AI, and how you can use it ethically for Google Maps data extraction.

Based on the original video:

Why Scrape Google Maps Data?

Access to up-to-date business data is invaluable for sales, outreach, and competitor research. Google Maps houses public listings that include business names, phone numbers, addresses, websites, reviews, and more. With the right scraping tool, you can compile this information into actionable lists for cold emailing, prospecting, or building location-based datasets—all without manual copying.

But scraping Google Maps isn’t just about quantity; it’s also about the quality of insights you can derive when the data is enriched and structured for your needs. That’s where “scrape table” comes in.

Introducing Scrape Table: A Free Google Maps Data Scraper

Developed as a side project, “scrape table” was originally meant to be a paid SaaS offering, targeting users who want quick, enriched data from sources like Google Maps, LinkedIn, and job boards. Built by a solo founder with the help of AI tools like ChatGPT and Claude, the project has morphed into a completely free beta tool for Google Maps scraping, available openly for anyone to use.

The tool is hosted on the developer’s AI company website and brings the core value—extracting business listings from Google Maps in seconds—without the need for payment or complex sign-ups.

Features Overview: What Can Scrape Table Do?

Here’s what sets Scrape Table apart for users interested in Google Maps data:

• Free, unlimited scraping of Google Maps business listings
• Support for multiple search approaches: interactive map or direct link input
• Flexible keyword targeting (e.g., “real estate”, “pizza”, “HVAC”)
• Results downloadable in CSV, Excel, or JSON formats
• Comprehensive business data: name, address, phone, website, reviews, ratings, open hours, and more
• Multi-location support—scrape data across URLs (e.g., multiple cities or regions)

How to Use Scrape Table for Google Maps Data Extraction

The tool offers a simple yet robust workflow:

• Interactive Map Method: Pinpoint locations on an embedded map, save up to five URLs for targeted scraping.
• Direct Link Input: Paste one or more Google Maps location links directly, ideal for advanced or bulk workflows.
• Keyword Entry: Input your niche or business type—such as “nail salons” or “restaurants.” The tool pulls all matching listings from your selected regions.

After you initiate a search, the backend automation collects data within seconds (typically 10–60 seconds, even for large datasets). Once done, the system reports the total results and allows instant download in your chosen file format.

Business Data Unlocked: What Kind of Information Can You Get?

After scraping, your download will typically include columns such as:

• Business ID
• Name and full address
• Phone number
• Website URL and Google Maps Place link
• Review count and ratings
• Time zone
• Keywords relevant to search
• Price level (for restaurants: $–$$$)
• Open hours and schedule
• Verification status
• Geographic markers: city, state, latitude, longitude

This rich database lets users slice, sort, and filter based on their outreach or research targets. If you’re after more context on building cold outreach lists, check out this guide on free unlimited Google Maps data scraping for strategies and compliance tips.

Case Studies: Real-World Google Maps Scraping in Action

The video demo shows quick searches for diverse keywords and geographies:

• Real Estate in Florida: Scraping from Tampa and Palm Beach yields 676 real estate business listings in less than a minute.
• Pizza in Florida: From pizza shops to national franchises, 139 entries extracted from a single region search.
• HVAC in Washington, D.C.: Nearly 900 HVAC contractors and related businesses compiled for localized B2B prospecting.

Each experiment demonstrates just how quickly the tool identifies and aggregates significant lead volumes—empowering users who would otherwise need hours of manual, copy-paste research.

Technical Insights: AI-Powered SaaS Built by a Founder, Not a Coder

One of the most inspiring aspects shared in the video is the development journey. The founder, without extensive coding experience, brought Scrape Table to life using AI tools and low-code technologies, including:

• Frontend: React.js web application, hosted on Vercel
• Backend: Firebase for user authentication, database, and serverless functions
• Payments Engine (legacy): Stripe integration for credit purchases (now unused in the free version)
• Rapid Prototyping: Extensive use of prompts in ChatGPT and Claude to generate front-end elements, authentication flows, and utility scripts

Originally developed as a monetized SaaS, Scrape Table was designed with a full dashboard: user login (Google/email), credit management, feedback/issue tracking, and detailed activity/task logs. While this paid infrastructure is on hold, the current public version preserves the core scraping capabilities for free.

Behind the Scenes: Application Architecture and Dashboard Experience

The dashboard, as showcased in the video, provides multiple tabs for data sources (planned for various platforms), credit management, download task history, user feedback, and account settings. Notably:

• Google/Firebase handled login—users could sign up, verify emails, and manage billing directly
• Stripe enabled dynamic credit purchasing for lead downloads (now suspended)
• All tasks (successful downloads, failed extractions) were logged for tracking and troubleshooting

The goal was to deliver a smooth, all-in-one experience, whether scraping Google Maps, LinkedIn, or job boards.

Data Flow and Backend Simplicity Powered by AI

The backend services relied heavily on Firebase for scalability and ease:

• Authentication: Seamless integration for email and Google sign-in
• Data storage: Every scrape request and resulting dataset stored with user context
• Credit system: Firebase Functions triggered credit assignment, validation, and Stripe payment webhooks
• Free Credits: New sign-ups were gifted starter credits automatically

Crucially, most of the code was built by “prompt engineering”—the founder simply asked generative AI (ChatGPT) to create login forms, route structures, and payment logic, resulting in rapid deployment with minimal traditional coding.

Ethical Considerations and Limitations

Web scraping utilities—especially those that automate Google Maps—should always be used responsibly. Always review relevant terms of service and ensure compliance with local privacy regulations. For B2B lead generation, use public business data strictly for ethical outreach and never for spam or unauthorized use.

The developer emphasizes that the SaaS is still in experimental beta, and its ongoing operation incurs costs. Donations are welcomed but not required. As with all community-built scraping tools, stability, long-term support, and feature growth depend on user feedback and involvement.

Expanding Your Scraping Toolkit

If you’re supplementing your scraping stack, check out these resources to broaden your lead generation strategies:

• How to Scrape Google My Business Leads Fast — A hands-on guide for quickly gathering and organizing GMB data for targeted local marketing.

Whether you’re prospecting for HVAC contractors, real estate agents, or pizza shops, having fast access to structured, verified data gives you a unique market edge.

Key Takeaways and Next Steps

• Scrape Table provides a no-cost, frictionless Google Maps scraper for collecting large volumes of business data
• Setup is intuitive: select locations, enter keywords, and download results in formats compatible with Excel, CRMs, or enrichment tools
• The tool was developed by a non-coder using modern AI, showing that anyone can build specialized SaaS apps with the right mindset and resources
• Be mindful of compliance: use public data ethically, and support free projects if they add value to your workflow

Ready to try it out? The developer provides a direct link to the free tool and open-sources both the legacy and current codebases for transparency and community collaboration.

FAQ: Google Maps Scraping and the Scrape Table Project

How does Scrape Table extract Google Maps business data?

Scrape Table uses a combination of interactive maps and URL inputs, combined with backend automation, to collect business listings. Users specify locations and keywords, and the tool fetches matched businesses—including names, contact info, reviews, and more—within seconds.

Is using Scrape Table legal and ethical?

Google Maps data is generally public, but always ensure your use conforms to Google’s terms and applicable data privacy laws. Use the data for legitimate business outreach and never for unsolicited or unethical contact.

What formats can I download Google Maps data in?

Scrape Table supports CSV, Excel (XLSX), and JSON downloads—making it easy to import scraped results into sales platforms, spreadsheets, or analytics tools.

Can I scrape data from other platforms like LinkedIn or job boards?

The current beta version is focused on Google Maps, but the original SaaS infrastructure was designed to eventually support sources like LinkedIn and job portals. Future development or community contributions may revive these features.

Who can benefit from using Scrape Table?

Anyone in sales, local SEO, lead generation, or research looking for large, up-to-date datasets of business listings—such as agencies, marketers, entrepreneurs, or developers—will find this tool valuable.