Product Scraper for Custom Sites

@vishal1145

Your anti-bot measures will determine whether this scraper runs for 3 days or gets blocked in 3 hours. Most e-commerce platforms flag scrapers that hit pagination endpoints too aggressively or reuse the same User-Agent string across 500 requests. If these sites use Cloudflare or DataDome, you'll need rotating proxies and session fingerprinting to avoid CAPTCHA walls. Before I architect the solution, I need clarity on two things: Are any of these sites behind login gates that require session persistence, or can I scrape everything as an anonymous visitor? If login is required, do you have test credentials, or do I need to reverse-engineer the auth flow? What's the total product count you're expecting across all sites - 5K products or 500K? This determines whether I use Scrapy's distributed crawling or a simpler BeautifulSoup loop, and whether we need database staging before CSV export. Here's the architectural approach: - PYTHON + SCRAPY: Build a multi-spider framework with custom middleware for request throttling (2-5 second delays per domain), rotating User-Agent headers, and automatic retry logic for 429/503 responses to stay under radar. - SELENIUM + HEADLESS CHROME: Handle JS-rendered prices and lazy-loaded images by triggering scroll events and waiting for DOM mutations before extraction - prevents missing data on React-heavy storefronts. - MYSQL STAGING TABLE: Load raw scraped data into a normalized schema first, then run SQL deduplication queries on SKU + title fuzzy matching before CSV export - catches variants listed under slightly different names. - PROXY ROTATION: Integrate residential proxy pool if sites show aggressive rate limiting during initial test runs - I've used Bright Data and Oxylabs for similar jobs without triggering blocks. - VALIDATION SCRIPT: Automated row-level checks for null fields, price format consistency, and image URL reachability - generates the summary report you need for acceptance testing. I've built 8 production scrapers that handled 50K-2M products each, including one for a price comparison engine that scraped 12 competitors daily without getting blacklisted. The 3-day timeline is tight but doable if the sites don't require complex CAPTCHA solving. Let's jump on a 10-minute call to confirm site complexity before I commit to the delivery schedule.

@gameprogrammer13

Hello, I have reviewed your requirements for extracting and cleaning product data from custom e-commerce sites. I am well-positioned to handle the dynamic content and pagination challenges you described while ensuring high data integrity. To keep crawls efficient and polite, I implement adaptive request throttling, randomized headers, and session management to mimic natural traffic patterns. My focus is on delivering clean, validated datasets ready for immediate use. I am ready to begin.

@loxtic

Hello, how are you doing? I’ve built robust data extraction pipelines for e-commerce sites using Python, BeautifulSoup, Selenium, and Scrapy to deliver clean CSVs with all required fields, deduped and validated. I focus on reliable run times, clear scripts with comments, a concise README, and a quick validation summary. I’ll handle anti-bot basics like throttling, rotating headers, and controlled proxy use to stay polite to hosts. Let me know further if interested

@haitham1996

Hi, This fits my workflow very well. I can build a robust Python scraping pipeline that extracts, cleans, validates, and exports complete product datasets from custom e-commerce platforms with high accuracy and reusable architecture. My approach combines Scrapy for scalable crawling, BeautifulSoup for fast parsing, and Selenium/Playwright only where dynamic rendering or login-protected content is required. The pipeline will handle pagination, lazy-loaded assets, variant extraction, retries, duplicate removal, and structured CSV generation cleanly. What I’ll deliver: • Clean CSV with titles, prices, SKUs, stock, variants, descriptions, and image URLs • Reusable Python scraper modules with comments • Retry/error logging + validation summary • Anti-bot handling with throttling, rotating headers, and polite crawl strategy • README with setup instructions and requirements I can deliver the first extraction pass within 48 hours and finalize the validated dataset shortly after. Looking forward to discussing the target sites and data structure.

@kshubham102

Hello I already have to production ready system with me. The system which I have get all the necessary techs and requirements that you wants. Feel free to reach out to me. I will show you demo version. Best Regards Shubham Sharma

@Premiya3

Dear client, I can extract complete product information from your custom e-commerce sites and deliver a clean, well-structured CSV with titles, prices, SKUs, stock status, image URLs, descriptions, and variant data. Using Python, I will combine BeautifulSoup for fast parsing, Selenium for dynamic or login-protected elements, and Scrapy for efficient crawl management. The scripts will handle pagination, lazy-loaded images, and JS-rendered prices while maintaining high accuracy and minimal runtime. The deliverables will include a validated CSV with no empty rows, reusable Python scripts with comments, a README for setup and execution, and a summary report showing total products, duplicates removed, and any pages that failed after retries. Anti-bot measures will include polite crawling with throttling, randomized headers, and optional proxy rotation to prevent blocks while respecting each host. I can deliver the first pass within two days and the final verified file by day three, ensuring ≥98% field completion and ready-to-use data for your workflow. Best regard,

@erica486

The important part here is keeping the extraction accurate across different site structures and dynamic elements. Projects like this usually fail because pricing, variants, lazy-loaded images, or paginated products are only partially captured during the crawl. I’d use Scrapy for the main crawling and data pipeline, with Selenium or Playwright only where JavaScript rendering or login flows are required. The extraction process would include validation, de-duplication, and field normalization so titles, SKUs, variants, pricing, stock status, and image URLs remain consistent before export to CSV. To keep the crawl stable, I use throttling, rotating headers, retry backoff, and session handling where needed. Proxy rotation would only be introduced if the target sites actively rate-limit requests. The goal is reliable extraction without putting unnecessary load on the hosts. The final delivery will include the cleaned CSV, reusable Python scripts, requirements documentation, and a validation summary showing totals, duplicates removed, and failed pages after retries. I can start immediately and deliver the first pass within two days with final validation completed by day three.

@vishnus58

Hi, I am a Machine Learning Engineer and Backend Developer with experience in Python, Flask, AI model training, data science, IoT, and ESP-IDF development. I have worked on real-world projects involving AI-powered systems, REST APIs, database integration, embedded systems, and full-stack web applications. I can deliver clean, scalable, and efficient solutions based on your project requirements. I focus on proper communication, optimized code, and timely delivery. I am confident I can help you complete this project successfully. Skills: • Python & Flask Backend • Machine Learning & AI Models • Data Science & Automation • ESP32 / ESP-IDF Development • REST APIs & MySQL • Full Stack Web Development Looking forward to working with you. Best regards, Vishnu S.

@charleyk2

As an awardee of the Full-Stack Web Developer of the Year, I have extensive experience with complex web scraping tasks using Python and SQL. With deep expertise in utilizing libraries such as BeautifulSoup, Selenium, and Scrapy to extract and clean data from websites, I am confident in providing you with the comprehensive CSV file you desire. My scripts always demonstrate a meticulous, well-commented approach for ease-of-reuse. Committed to meeting your swift deadline, I will start with setting up environment variables that prevent anti-bot-techniques using throttling, rotating headers and if necessary, employing proxies. To ensure I provide the total products found and remove any duplicates, I will rigorously validate each dataset on pages that successfully load after retries. Moreover, my experience with large-scale cloud deployments (specifically AWS) positions me perfectly to make your crawl polite to each site. Through intelligent management of servers'/containers' resources using Kubernetes, Firestore and AWS Lambda Functions, I can also create a secure web application where you can initiate/edit runs anytime you require scraping or background info on progress. Efficiency pairing AI-powered monitoring tools with scalable backend

@DreamDevil07

Hello, I am highly interested in working on your AI project. I have experience in AI tools, machine learning, automation, and problem-solving. I can deliver high-quality work within the given timeline and ensure accurate, efficient, and reliable results according to your requirements. I would love the opportunity to discuss the project further and start working as soon as possible. Thank you.