I'm building an ecommerce catalog ingestion workflow and need reliable site extraction

A reliable ecommerce catalog extraction workflow usually needs two layers:

1. Discovery / crawling: find product URLs and category pages.
2. Extraction / normalization: pull structured product data from each product page and map it to your catalog schema.

Here’s a practical approach that works well in production.

1) Prefer structured data first

Most ecommerce sites expose product info in:

• JSON-LD (<script type="application/ld+json">)
• Microdata / Open Graph
• Embedded app state like __NEXT_DATA__, window.__PRELOADED_STATE__, etc.

For each page, try in this order:

1. JSON-LD Product schema
2. Embedded product state
3. Fallback HTML selectors
4. Headless browser rendering only when needed

This keeps extraction fast and resilient.

2) Build a site profile per domain

For each store/domain, store:

• Product URL patterns
• Category/listing page patterns
• Pagination style
• Variant behavior
• Presence of structured data
• Anti-bot behavior
• Locale/currency conventions

A simple “site adapter” or “profile” layer saves a lot of pain later.

3) Extraction fields to standardize

At minimum, normalize:

• sku
• gtin / mpn if available
• brand
• name
• description
• images[]
• price
• currency
• availability
• variants[] (size/color/etc.)
• categories[]
• product_url
• canonical_url
• breadcrumbs[]

4) Make extraction idempotent

Use a stable product key:

• Prefer sku/gtin
• Otherwise derive from canonical URL + site ID

Track:

• raw payload
• parsed payload
• extraction confidence
• timestamp
• source URL
• parser version

This makes reprocessing and debugging much easier.

5) Use validation and scoring

Add rules like:

• price must be numeric and > 0
• product name length within bounds
• image URLs valid and non-placeholder
• availability from known enum
• currency from ISO-4217 set

Assign a confidence score and route low-confidence pages to review or fallback parsing.

6) Handle variants carefully

Variants are a common failure point. Decide whether your catalog stores:

• one parent product + variant children
• one row per purchasable variant

Look for:

• option dropdowns
• query params
• embedded variant arrays
• color swatches / size selectors

7) Crawl responsibly

Use:

• robots.txt checks where appropriate
• rate limiting
• exponential backoff
• retries with jitter
• caching
• deduplication
• user-agent transparency if required

8) Recommended workflow architecture

A solid pipeline looks like:

Seed URLs → Crawler → HTML fetcher → Structured-data parser → Variant resolver → Fallback extractor → Normalization → Validation → Deduplication → Storage / downstream sync

9) Libraries and tools

Depending on your stack:

• Python: httpx, BeautifulSoup, lxml, selectolax, playwright
• JS/TS: playwright, cheerio, axios
• Structured data: JSON-LD parsing utilities
• Queues: RabbitMQ, SQS, Kafka, Celery, BullMQ

10) A robust rule of thumb

If you need “reliable,” aim for:

• 80–90% extraction from structured data and page source
• 10–20% fallback via HTML / rendered DOM
• minimal dependence on brittle CSS selectors

If you want, I can help you design:

• a catalog ingestion architecture
• a product page extraction schema
• or a sample crawler/parser implementation in Python or TypeScript.