SBEEC - Data Scraping Tool
Scalable data-scraping tool with analytics.
- Client
- SBEEC Pvt Ltd
- Platform
- App · Desktop
- Sector
- SaaS
What we did Development · QA · Project Management
Built with
The stack behind SBEEC - Data Scraping Tool
Node.js
Python
Results
What the client had at the end of the engagement.
Increased scraping throughput more than tenfold, cut server load, and reduced ongoing maintenance cost.
Working on something similar? Tell us the problem and we will tell you how we would approach it.
Talk to an engineerBefore
Background
The client's in-house engineering team built an initial desktop product, but the system's complexity hit a bottleneck. They required seasoned technological expertise to stabilize, scale, and optimise the data extraction architecture to handle far larger datasets.
After
What we did
Operating under strict Scrum methodologies, we rebuilt the legacy application so it scales and can be extended, and added an analytics system to monitor extraction efficiency.
Inside the product
The parts that took the work, and what each one does.
- High-Performance Scraping Engine
- A Python-powered core that extracts complex datasets at 10x the speed of the legacy system.
- Extraction Analytics Dashboard
- A real-time monitoring system that tracks the effectiveness and success rate of active data scraping tasks.
- Extensible API Architecture
- A modular design so external APIs and other data sources can be added later without a rewrite.
- Automated Server Load Balancing
- Optimized system performance protocols that drastically reduce backend strain during peak extraction periods.
How we verified it
The checks that ran before this went in front of users.
- Test framework
- Automated test scripts built to validate data accuracy and parser resilience against DOM changes on target sites.
- Load and stress
- Pushed extraction limits to validate the 10x performance increase, ensuring the application remained responsive under extreme server loads.
- Security
- Implemented robust error handling and IP rotation/proxy management protocols to prevent blocks during intensive scraping operations.
How it is put together
Layer by layer, and why each choice was made.
- Front end
- A streamlined, responsive desktop interface focusing on data visualization and task management.
- Back end
- A hybrid architecture utilizing Node.js for asynchronous task orchestration and Python for heavy-lifting data extraction and parsing algorithms.
- Data
- Optimized database querying structures to handle rapid, high-volume data ingestion without locking the primary threads.
- Infrastructure
- The system was refactored for modularity, allowing the data gathering module to easily integrate with external third-party APIs in future iterations.
Want something like this?
Tell us what you are trying to build. We will tell you how we would approach it, what it would take, and whether we are the right team for it.
