E-commerce: guides & tutorials

TL;DR

• One docker-compose.yml: Medusa.js (headless commerce) + Mautic (email automation) + Chatwoot (customer support) + PostgreSQL + MariaDB + Redis + Caddy
• Measured idle RAM: ~2.8 GB; peak: ~4.2 GB (10 concurrent Chatwoot conversations + active Mautic campaigns)
• Minimum Liquid Web tier: 8 GB Managed VPS (~$33–$40/mo)
• Shopify Basic ($79/mo) + Klaviyo ($45/mo) + Gorgias ($10/mo) = $134/mo plus transaction fees; this stack: ~$33/mo with 0% transaction fees

Shopify bundles storefront hosting, payment processing, and a basic admin — but the moment you add email automation (Klaviyo) and customer support (Gorgias), costs compound fast. This guide deploys the open-source equivalents: Medusa.js for headless commerce, Mautic for email and marketing automation, and Chatwoot for multi-channel customer support — on a single Liquid Web 8 GB VPS, wired together so new orders automatically flow into Mautic campaigns and Chatwoot agents can look up order history without leaving the conversation.

Amazon is the primary source for product pricing, review sentiment, and competitive research. Scraping it manually is notoriously difficult — Amazon deploys heavy bot protection, JavaScript rendering, and geo-pricing.

Apify's Amazon scrapers handle all of this with residential proxies, CAPTCHA solving, and structured output. No code required.

Legal note: Amazon ToS prohibits unauthorized scraping. Only scrape publicly displayed pricing data for research, price comparison, and competitive intelligence. Never create accounts programmatically or access private data.

E-commerce data collection in 2026 spans four core types: product catalog, pricing, reviews and ratings, and inventory. Each has different crawl frequency needs, anti-bot considerations, and target-specific approaches. This guide covers data types, price monitoring architecture, target breakdowns (Amazon, Shopify, WooCommerce), review scraping strategies, inventory tracking, Apify Store Actors, data schemas, and a price alert system. For pre-built scrapers, the Apify Store offers Amazon, Shopify, and Walmart Actors. For anti-bot heavy targets, Bright Data provides Scraping Browser and datasets.

Price monitoring is one of the highest-ROI applications of web scraping. Retailers, brands, and e-commerce teams use it to:

• Track competitor pricing in real time
• Automatically match or undercut competitor prices
• Identify price drops for affiliate marketing alerts
• Monitor MAP (Minimum Advertised Price) compliance

This guide builds a complete price tracker: scraping with Apify, storage in a database, and alerting via Make.com.

Use Firecrawl to scrape product pages into structured JSON. Map product URLs first, extract with a schema (name, price, stock, brand), validate and normalize, then upsert to your storage. For heavy anti-bot sites or very high volume, Apify ecommerce actors may be a better fit.

Start Firecrawl product scraping →

Amazon aggressively rate-limits automated traffic: datacenter IPs, sequential ASIN walks, and headless fingerprints often trigger CAPTCHAs or empty responses. For production price monitoring, MAP enforcement, or catalog analytics, teams usually choose either a managed dataset/API (less parser work) or a flexible Actor/scraper (more control). Two common options are Bright Data e-commerce scraping products and Apify Amazon Actors.

In April 2026, Walmart has become the primary battleground for retail arbitrage and market intelligence. But for developers, it remains one of the most difficult targets on the web.

Unlike other retailers that block you with a clear 403 Forbidden, Walmart is famous for the "Silent 200." You send a request, you get a "Success" status code, but the HTML payload is missing the actual price, stock, and SKU data. To your monitoring script, everything looks fine; to your business, the data is useless.

This guide explains how to build a production-grade Walmart scraper using Apify that bypasses shadow-bans and delivers verified data.

If you trade on price spreads or run arbitrage between marketplaces, you need Walmart numbers you can trust—not a pretty HTTP status with junk in the body.

Walmart’s edge is aggressive bot filtering. A naive script may “succeed” on paper while returning HTML that omits the real product state.

This guide walks through how that failure mode shows up and how to pull reliable pricing and availability with the Apify platform.

Google Shopping pulls together a wide mix of sellers—Shopify stores, big-box retailers, and smaller shops—in one results surface.

That makes it useful for price monitoring and competitive checks: you can compare how a product shows up across sellers and regions. The catch is the page changes often, a lot of markup is loaded in the browser, and Google rate-limits aggressive scraping.

Here’s a practical way to get structured JSON out of Google Shopping with the Apify platform.

Amazon product prices can change dozens of times per day. Marketplace sellers who respond to price shifts manually lose Buy Box time (and revenue) every hour they're asleep. For any e-commerce business selling on competitive marketplaces, automated price monitoring isn't optional — it's fundamental operations infrastructure.

This guide covers how to build a full price monitoring system: what data to collect, how to automate the workflow, and how to set up price-change alerts that trigger actions without human intervention.