Map data scraper for lead generation
An automated system that collects business listings and location data from map services, then cleans, deduplicates and geocodes it into a lead database.
- Industry
- Data collection and lead generation
- Services
- Web scraping, data pipelines, data cleaning
- Stack
- Python, Selenium, Scrapy, PostgreSQL
- Timeline
- About 2.5 months
The problem
The client needed broad location data for a business intelligence platform, but the available sources were fragmented and expensive. Collecting it by hand was slow and error-prone, and commercial APIs came with high costs and usage limits that did not fit their volume.
Anti-scraping measures, rate limits and JavaScript-rendered pages made automation difficult. Duplicates and inconsistent formatting made it harder still to build a reliable database for analysis.
How we approached it
We built a scraping platform that uses proxy rotation and request throttling to collect data reliably, with headless browsers for JavaScript-rendered pages. Collection stays within rate limits and the sites' terms of service.
Validation and cleaning pipelines bring accuracy to 98%. Deduplication and normalisation turn raw results into structured, analysis-ready records, and a distributed setup spreads the work across multiple sources.
Monitoring, automatic retries and backoff keep collection running when a source fails temporarily or starts rate limiting.
What we built
Automated scraping
Scheduled data extraction from multiple map sources without manual work.
Data validation
Quality checks on every record before it reaches the database.
Deduplication and geocoding
Duplicate listings are merged and locations are normalised to coordinates.
Throughput
Processes 1,000+ records per hour.
Retry and backoff
Failed requests are retried with backoff so temporary errors do not lose data.
Structured export
Clean data delivered in multiple formats for existing systems.
Tech stack
- Python
- Selenium
- BeautifulSoup
- Scrapy
- PostgreSQL
- Pandas
- Proxy Rotation
- Headless Browser
- Data Cleaning
- AWS
- Docker
How the project ran
- 011 week
Research and planning
Target analysis, scraping strategy and architecture design.
- 021 month
Scraper development
Scraping scripts, proxy setup and anti-detection measures.
- 032 weeks
Data processing
Cleaning, validation and deduplication pipelines.
- 042 weeks
Testing and tuning
Performance testing, accuracy checks and optimisation.
- 051 week
Deployment and execution
Production deployment and the extraction runs.
Results
The system collected and processed more than 100,000 location records at 98% accuracy, at around 1,000 records per hour. Automated cleaning removed duplicates and kept formatting consistent across sources.
Data exports in several formats plug into the client's existing systems, and the setup supports ongoing collection and updates. The client now has the location data it needs for market analysis and planning at a fraction of the cost of commercial data providers.
- 100,000+ records collected
- 98% data accuracy
- 1,000 records processed per hour
- Structured export in multiple formats
What is the map data scraper?
It is an automated system that collects business listings, location details and geographic data from map services. It cleans, deduplicates and geocodes the results and exports them for lead generation and business intelligence.
What did the project achieve?
The system collected more than 100,000 map records at 98% accuracy, with automated cleaning and deduplication along the way.
What were the main challenges?
Processing and storing data at large scale, removing duplicates and inconsistent formatting, and meeting strict data quality and validation requirements.
How did you solve them?
We used a distributed architecture for large-scale processing, deduplication and normalisation steps for messy data, and validation and cleaning pipelines to check every record.
How long did the project take?
About two and a half months across five phases: research and planning, scraper development, data processing, testing and tuning, and deployment and execution.
Which technologies did you use?
Python, Selenium, BeautifulSoup, Scrapy, Pandas, PostgreSQL, AWS and Docker, with proxy rotation and headless browsers.
What support do you provide after launch?
Bug fixes and updates, performance monitoring, security patches, improvements based on user feedback, and ongoing technical support and maintenance.
Can you build something similar for us?
Yes. We can build a data collection system around your sources, volumes and the way you plan to use the data.
What difference did it make to the client's business?
Data extraction became 6.5 times faster. The client now collects 2.6M+ records a week at 99% accuracy, which helped them expand into 12 new markets in under 9 months.
Need a reliable data collection pipeline?
Tell us which sources you need and what the data is for. We will tell you what is practical and how we would build it.