How to Scrape Rakuten Data: Complete Guide for 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Scrape Rakuten's public product listings using AlterLab's API with automatic anti-bot handling. Start at T1 tier and let the API promote automatically if needed. Extract structured data via CSS selectors or Cortex AI for JSON output. Respect rate limits and robots.txt.

Why collect e-commerce data from Rakuten?

Rakuten hosts millions of product listings across electronics, fashion, and home goods. Engineers scrape this public data for:

• Price monitoring: Track competitor pricing fluctuations across categories
• Market research: Analyze product availability and description trends
• Data enrichment: Supplement internal catalogs with public attributes like ratings and specifications

Technical challenges

Rakuten implements standard e-commerce anti-bot measures including rate limiting, header validation, and JavaScript challenges. Raw HTTP requests often fail with 403 or 429 responses. AlterLab's Smart Rendering API handles these through:

• Automatic proxy rotation with residential IPs
• Header normalization to mimic real browsers
• Tiered rendering (from curl to full browser) based on challenge detection
• JavaScript execution for dynamic content loading

Quick start with AlterLab API

See the Getting started guide for SDK installation. Below are examples for scraping a public Rakuten product page.

Python example:

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://rakuten.com/product/example")
print(response.text)

Node.js example (MUST include this):

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://rakuten.com/product/example");
console.log(response.text);

cURL example:

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://rakuten.com/product/example"}'

Extracting structured data

Rakuten product pages follow consistent HTML patterns. Use browser dev tools to identify selectors for:

• Product title: h1.product-title
• Price: .price-current
• Rating: .rating-value
• Description: #product-description

Example Python extraction:

import alterlab
from parsel import Selector

client = alterlab.Client("YOUR_API_KEY")
html = client.scrape("https://rakuten.com/product/example").text
selector = Selector(text=html)

data = {
    "title": selector.css("h1.product-title::text").get(),
    "price": selector.css(".price-current::text").get(),
    "rating": selector.css(".rating-value::text").get(),
    "description": selector.css("#product-description::text").get()
}
print(data)

Structured JSON extraction with Cortex

For schema-defined output without CSS selectors, use AlterLab's Cortex AI extraction. Define a JSON schema for typed results:

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://rakuten.com/product/example",
    schema={
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "price": {"type": "number"},
            "rating": {"type": "number"},
            "description": {"type": "string"}
        }
    }
)
print(result.data)  # Typed JSON output

Cortex handles:

• Automatic type conversion (strings to numbers)
• Missing field null handling
• Multi-language text normalization
• Unit standardization (e.g., converting "¥1,200" to 1200)

Cost breakdown

AlterLab's pricing scales with rendering complexity. Rakuten typically requires T2-T3 tiers due to anti-bot measures. See AlterLab pricing for full details.