Selleryar
Automated analytics pipelines, scraping systems, APIs, and scalable infrastructure for seller performance tools.
AI and data tools for e-commerce sellers
System overview
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Continue to project detailsScrapy, BeautifulSoup and Selenium acquire catalog data for Python, Pandas and NumPy analysis and seller performance tools. REST APIs use FastAPI, Flask and Django. Caching, PostgreSQL query tuning, MongoDB schema work and Docker, Kubernetes and Nginx scaling support delivery. The work covered over one million data points, expanded catalog coverage threefold and reduced query latency from 900 to 300 milliseconds while handling fivefold traffic spikes.
Context
Selleryar focused on seller performance optimization, requiring reliable data acquisition, analytics pipelines, and performant seller-facing features.
Problem space
The product needed broader catalog coverage, lower reporting effort, lower data acquisition costs, and scalable infrastructure during traffic spikes.
Solution architecture
Built analytics pipelines over 1M+ data points using Python, Pandas, and NumPy.
Created automated scraping pipelines and REST APIs with Scrapy, BeautifulSoup, Selenium, FastAPI, Flask, and Django.
Improved throughput through Docker/Kubernetes scaling, Nginx load balancing, server-side caching, PostgreSQL query tuning, and MongoDB schema optimization.
- Catalog coverage
- 3x
Expanded product data acquisition reach.
- Lower query latency
- 67%
Reduced average latency from 900ms to 300ms.
- Traffic spike sustained
- 5x
Handled concurrent load without downtime.
Technology stack
- Python
- Pandas
- NumPy
- Scrapy
- Selenium
- FastAPI
- Django
- PostgreSQL
- MongoDB
- Docker
- Kubernetes
- Nginx