Country-wide Google Maps Data Extraction

@diektech

Hi — Elias here from Miami. I see you're looking to extract data from Google Maps for an entire country. This type of project primarily aims to gather actionable insights from a vast dataset, which can be invaluable for various applications. The real technical challenge here lies in handling the sheer volume of data while ensuring reliability and compliance with Google’s terms. What usually matters most here is the efficiency of the scraping process and managing the data flow. A common issue in such systems is dealing with rate limits and ensuring that the data remains accurate and up-to-date. To approach this, I would structure the solution using Python with libraries like Selenium for scraping, combined with PostgreSQL for efficient data storage. This will help ensure that the system is scalable and maintainable. I’ve implemented similar data extraction systems before, which helps me anticipate potential pitfalls. A few questions to better understand the scope: Q1 – What specific data points do you need from Google Maps? Q2 – Are there particular regions or areas of focus within the country? Q3 – How do you plan to use the extracted data, and what are your expectations for updates? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@onkarkulkarni8

Hi, I your "Country-wide Google Maps Data Extraction" project description in detail and undertood your requirements. I've worked on many PHP projects in recent times. So I am confident on achieving your expected Goals. Please initiate a communication thread to discuss further and start with the project. ⭐ 5.0/5 from a recent client: "Project was delivered before Time with Best professional Knowledge One could ever held. Thanks for the support" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@Azursol

Greetings, Collecting extensive business data from Google Maps across an entire country is a complex task that requires a robust and efficient scraping system. The challenge lies in ensuring comprehensive coverage while managing duplicates and handling dynamic content effectively. How to achieve this: - Implement a scalable scraping architecture using tools like Scrapy or Selenium, designed to run continuously and handle pagination. - Use geographic segmentation to systematically cover all areas, ensuring no locations are missed. - Integrate a duplicate detection mechanism to maintain data integrity and streamline the storage process in PostgreSQL. With my experience in large-scale web scraping and proficiency in Python and PostgreSQL, I'm confident in delivering a reliable solution. I’ve worked on similar projects, including data extraction for various business categories, ensuring high-quality and complete datasets.

@TeraByte26

Hi Ahmed, I will deliver a complete dataset for an entire country with all specified categories and data fields. I commit to completing this project within your budget of 250-750 USD. Can I start with a free sample? Waiting for your response in chat! Best Regards.

@mayr81

Build a country-scale Google Maps extraction pipeline that reliably captures business/place records and stores them in PostgreSQL. Approach: use Playwright (browser automation) for dynamic results, orchestrated with a resilient job queue for systematic coverage across cities/regions and categories. Implement pagination + infinite-scroll handling, session/proxy management, and anti-bot adaptive retries. Ensure resume-after-interruption by checkpointing per region/category and persisting scrape state. Data model: normalized PostgreSQL schema for places, categories, hours, ratings/review counts, contacts, media, and source URLs. Add duplicate detection using canonicalization (name/address/geo rounding + URL fingerprints) and conflict resolution rules to avoid re-inserts. Reliability: structured logging, error classification, failed-request replay, rate-limit controls, and long-running operational safeguards. Provide configuration-driven category sets and geographic tiling so the scraper doesn’t rely on a single search query. Deliverables include complete source code, schema, pipeline, normalization/duplicates logic, operational docs, and an initial verified test run.

@bilal4240

I will build a country-scale Google Maps scraping pipeline using Python, Scrapy/Playwright, and residential proxy rotation to systematically extract complete business datasets directly into your PostgreSQL database. I will implement spatial grid-based geographic coverage with automated deduplication, exponential retry logic, and progress checkpointing to run reliably for extended periods without manual intervention. Regards, Bilal

@AndrewJSMAN

I’ve built two country-wide Google Maps scrapers last year using Playwright with Python and PostgreSQL bulk inserts, so this is right in my wheelhouse. I’ll implement a distributed Playwright cluster with headless Chrome nodes, geospatial partitioning across cities via GeoNames or OpenStreetMap, and a PostgreSQL schema with BRIN indexes for fast range scans on lat/lon. Scrapy + ScrapyRT will handle category-specific spiders, while a central task queue (Redis) manages resume/retry with exponential backoff and duplicate detection via MD5 hashes of normalized addresses. Logging will stream to Loki and failed requests auto-retry with rotating residential proxies. The pipeline will write to temp tables, validate row counts per city, then merge into the main dataset with ON CONFLICT DO NOTHING. I can start immediately. Thanks, Andrii.

@ianb65

Hi there, Country-scale Google Maps scraping needs a system that can keep discovering locations, recover from failures, and store clean records in PostgreSQL. I’ve strong expertise in solving exactly this type of data extraction problem, and I would build a Python-based pipeline with PHP-facing handoff only if needed, using Excel for any review/export workflows and Data Mining methods to expand coverage across cities, categories, and geo-grids instead of relying on one query. The implementation would include browser automation, deduplication, retry/resume logic, structured logging, and normalized storage for names, ratings, hours, URLs, and coordinates. Best regards, Ian

@ammarahmed20

With over 6 years of experience in web scraping, Python development, and PostgreSQL, I am well-equipped to take on your country-wide Google Maps data extraction project. As a skilled freelancer, I have successfully designed and implemented large-scale data collection systems that are capable of handling the very challenges you've outlined. My proficiency with technologies like Playwright, Puppeteer, and Selenium will substantially contribute towards building a reliable scraper for collecting business and place data from Google Maps. My aim is to not just complete the task at hand but to deliver something that runs without hitches in a production environment - a solution that captures all possible information while remaining versatile enough to adapt to future changes. The databases in previous projects I've completed have been similar which means that I am intimately aware of the challenges and intricacies involved. You can rely on me to design a custom solution that appropriately fits your PostgreSQL database and delivers highly valuable Google Maps dataset for every location you need intact.

@albertc1996

Hey, Country-scale Google Maps scraping breaks in two places — hitting rate limits when covering dense urban areas, and missing locations in rural zones where category searches return incomplete results. Both need a systematic geographic grid approach rather than just keyword searches. I'd use Playwright with rotating proxies and store everything in PostgreSQL with resume functionality built in from day one. Which country are we targeting — affects how I'd structure the geographic coverage strategy? 5+ years building backend systems that hold up under real load — systems that stay stable long after delivery. Django scrapers, FastAPI services, AI agents wired into LLMs, automation pipelines with n8n and Make, Kafka event-driven systems — different stacks, same habit of getting it right the first time. If there's a bug buried in the logic, I find it before it reaches production.

@khuzaima024

Hi, I have experience building large-scale Google Maps data extraction systems using Python, Playwright, and PostgreSQL, with automated retries, deduplication, and scalable geographic/category coverage. I can build a reliable country-scale pipeline with resume support, data normalization, logging, and efficient PostgreSQL storage to minimize missing locations. I can start immediately and provide a complete, maintainable solution tailored to your server and dataset requirements.

@SkillCrafts

Having successfully developed robust data extraction solutions for large-scale geographical datasets, including projects involving extensive location-based information, I understand the intricacies of building reliable scrapers for platforms like Google Maps. My experience with similar projects has equipped me with the foresight to anticipate challenges and implement efficient strategies to ensure data integrity and completeness, mirroring the ambitious scope of your country-wide data collection goal. My technical approach will leverage Python with libraries such as `BeautifulSoup` and `Scrapy` for efficient parsing and crawling. I will implement a distributed scraping architecture to handle the vast scale, employing proxies and user-agent rotation to maintain anonymity and avoid rate limiting. Data will be extracted systematically, focusing on key identifiers and attributes, and stored in a structured format (e.g., JSON or CSV) for easy integration and analysis. Error handling and retry mechanisms will be built in to ensure resilience. To ensure we align perfectly on the project's nuances, could you clarify the specific data fields you deem most critical for each business category? Additionally, what is your preferred method for handling potential data inconsistencies or missing information during the extraction process? I'm eager to discuss how my expertise can deliver this comprehensive dataset for you.

@miguelcen64

Absolutely , this is the kind of country-scale extraction project I like. I’d recommend a robust Python-based system built for scale, not a one-off scraper. My approach would combine Playwright for dynamic map interactions, PostgreSQL for storage, and a queue-driven architecture with checkpointing so the job can run continuously, recover cleanly, and avoid reprocessing. I’d design the crawler to sweep the country by grid, city, and category, then expand from discovered locations to reduce misses. Deduplication would be handled through normalized names, address fingerprints, coordinates, and place IDs/URLs where available. For reliability, I’d add structured logging, retry logic, request/session rotation, and resumable job state. The database schema would support raw captures plus cleaned entities, which makes updates and auditing much easier. I can deliver the full scraping system, schema, storage pipeline, docs, and an initial verified run. Miguel

@MohsinCodeCraft

Hi, I can build a scalable Google Maps data extraction pipeline with Python, PostgreSQL, Playwright/Scrapy, and a reliable queue-based architecture. I’ll design geographic/category-based collection instead of relying on a single query, with deduplication, normalization, retries, resume support, logging, and efficient PostgreSQL storage. For reliability and compliance, I can evaluate the official Google Places API or a suitable data provider alongside browser-based extraction where appropriate. I can provide the complete source code, database schema, configuration, documentation, and an initial country-scale test run. Estimated development time is 2–3 weeks depending on the target country and data volume; I can provide a precise cost after reviewing the scope and expected coverage. Please feel free to message me to discuss the project further. I’d be happy to share samples of my previous work in the chat. Thanks and regards, Mohsin

@joharashu0

Hi, I can build a **production-ready, country-scale Google Maps data extraction system** designed for long-running, reliable scraping and PostgreSQL storage. With **5+ years of experience** in Python, Playwright, Selenium, PostgreSQL, and automation, I focus on scalable scraping solutions rather than simple scripts. **My approach:** * Python + **Playwright** for browser automation * Grid/tile-based geographic crawling to cover the entire country * Multi-category discovery (Hotels, Restaurants, Banks, Schools, Hospitals, etc.) * Automatic resume, retry, and checkpointing * Duplicate detection using Place ID, coordinates, name, and address * Logging, error handling, and configurable crawl settings * Efficient PostgreSQL schema with normalized data **Deliverables:** ✔ Complete source code ✔ PostgreSQL integration ✔ Resume/retry functionality ✔ Duplicate handling ✔ Documentation & deployment guide ✔ Initial verification run I'm available to start immediately and can recommend the best architecture based on your target country, infrastructure, and completeness requirements. Looking forward to discussing your project! **Thanks,** **Ashutosh** Python | Playwright | PostgreSQL | Web Scraping | Automation

@amadorjay

Hi there, The challenge lies in efficiently scraping a comprehensive dataset from Google Maps while ensuring data integrity and reliability across an entire country. The focus should be on designing a resilient system capable of handling dynamic content, pagination, and duplicates without manual intervention. For this project, I recommend utilizing a combination of Scrapy for its robust scraping capabilities and PostgreSQL for efficient data storage. This will allow for systematic coverage of categories and geographic areas while logging errors and maintaining a smooth extraction process. To further refine the approach, how do you envision managing the rate limits imposed by Google Maps? Looking forward to discussing the details in chat.

@toqeer74

Hi, I can help you build a reliable scraper for collecting complete Google Maps data across the country. I've successfully managed similar large-scale web scraping projects, specifically targeting Google business data, using tools like Scrapy and Selenium. My approach will involve setting up a solid system to systematically cover all geographic areas and categories while efficiently handling pagination, duplicates, and interruptions. I've worked with PostgreSQL for data storage before, ensuring smooth integration and data normalization. This project would take about $[Price] in 15 days to deliver a complete system along with documentation and an initial test run. How do you envision the categorization of locations, and do you have any specific areas in mind to prioritize?

@sanddep5

I would be an excellent fit for your country-wide Google Maps data extraction project. As an experienced full-stack developer and an expert in web scraping, I have successfully completed several large-scale scraping projects involving massive datasets similar to what you require. Moreover, my proficiency in Python and PostgreSQL, coupled with my understanding of browser automation techniques through tools like Selenium and Scrapy, will ensure efficient data collection and seamless integration into your existing PostgreSQL database. To achieve a complete dataset for an entire country, I propose leveraging a combination of scraping technologies/APIs such as Playwright or Puppeteer designed to extract, automate, and store a massive amount of often-changing data from modern websites. An important aspect is avoiding reliance on a single search query; instead, I can devise a systematic approach that dynamically covers a range of cities and geographic areas encompassing all logical variations of search queries/combinations. Handling duplicates and missing data is crucial in any such project. I will design the scraper to intelligently identify and handle duplicates by using appropriate data normalization techniques. For missing data, the system will be able to log and notify me so that it can be addressed promptly.

@bhargav922002

Hello, I am writing to propose my services for the Country-wide Google Maps Data Extraction project. I specialize in web scraping and have experience utilizing technologies like Python and frameworks such as Scrapy and Selenium, which are well-suited for large-scale data collection tasks like yours. My approach will involve first inspecting the existing codebase to thoroughly understand the current setup. I will then attempt to reproduce any issues you may be facing. Once I have gained insight into the scraping environment, I will identify the root cause of any problems and implement targeted fixes, ensuring that the scraper can handle dynamic loading, pagination, and duplicate data effectively. Testing will be performed to confirm that related functionalities work seamlessly, and I will conduct regression checks to ensure stability after modifications. To better assist you, I would appreciate details regarding any error logs, specific reproduction steps you have encountered, access to your repository, and information about the scraping environment. These details will help me tailor my solution to fit your requirements effectively. Thank you for considering my proposal. I look forward to collaborating with you on this project to ensure a scalable, reliable scraping system that meets your expectations. Best regards.