I'm building a product catalog aggregator and want an API instead of scraping

Absolutely — building an API-first product catalog aggregator is the right move if you want reliability, scalability, and cleaner integrations.

Here’s a practical way to approach it.

1) Define the core API use case

You’ll usually want one or more of these:

• Search products across multiple sources
• Fetch product details by ID/SKU/UPC/URL
• Compare prices across merchants
• Normalize catalog data into a common schema
• Track availability and changes over time
• Ingest vendor feeds via API, CSV, XML, or webhooks

2) Recommended high-level architecture

A good setup is:

• Connectors / adapters for each source API
• Normalization layer to map each source into your standard model
• Matching/deduplication engine to identify the same product across sources
• Storage
  • relational DB for canonical catalog data
  • search index for fast querying
  • object storage for raw payloads
• Sync pipeline
  • scheduled pulls
  • webhook ingestion when available
  • retry/error handling
• Public API
  • search
  • product detail
  • merchant offers
  • bulk export

3) A clean canonical data model

You’ll want a unified product schema such as:

{
  "product_id": "canonical_123",
  "title": "Apple AirPods Pro (2nd Gen)",
  "brand": "Apple",
  "category": "Wireless Earbuds",
  "identifiers": {
    "gtin": "195949121234",
    "mpn": "MQD83AM/A",
    "sku": null
  },
  "attributes": {
    "color": "White",
    "connectivity": "Bluetooth"
  },
  "images": [],
  "offers": [
    {
      "source": "merchant_a",
      "price": 199.99,
      "currency": "USD",
      "availability": "in_stock",
      "url": "https://..."
    }
  ],
  "updated_at": "2026-10-08T00:00:00Z"
}

4) Matching products across sources

This is usually the hardest part.

Use a tiered strategy:

1. Exact identifiers: GTIN/UPC/EAN/ISBN, MPN, SKU
2. Strong text matching: brand + title + model
3. Attribute similarity: size, color, capacity, pack count
4. Fuzzy matching / ML for edge cases

You’ll want to keep a confidence score and allow manual review for ambiguous matches.

5) API design suggestions

Typical endpoints:

• GET /products?query=...
• GET /products/{id}
• GET /products/{id}/offers
• GET /merchants/{id}/products
• POST /ingest for vendor feeds
• GET /changes?since=...
• POST /match if you expose matching internally

Example search response:

{
  "results": [
    {
      "product_id": "canonical_123",
      "title": "Apple AirPods Pro (2nd Gen)",
      "best_offer": {
        "merchant": "merchant_a",
        "price": 199.99,
        "currency": "USD"
      }
    }
  ],
  "page": 1,
  "page_size": 20,
  "total": 124
}

6) Data source options

Instead of scraping, look for:

• Retailer/merchant APIs
• Affiliate feeds
• Marketplace APIs
• Manufacturer product feeds
• Distributor feeds
• GS1 / UPC lookup services
• Partner integrations

Many merchants also provide:

• price/availability feeds
• XML product catalogs
• inventory APIs
• affiliate product endpoints

7) Important operational concerns

• Rate limiting and backoff
• Caching
• Idempotent ingestion
• Schema versioning
• Audit trail for raw source data
• Data freshness / SLA per source
• Auth: API keys, OAuth, partner tokens
• Observability: job status, failure alerts, source health

8) Best stack choices

A common stack:

• Backend: FastAPI / Django / Node.js / Go
• DB: PostgreSQL
• Search: Elasticsearch / OpenSearch / Meilisearch
• Queue: SQS / RabbitMQ / Kafka
• Cache: Redis
• Storage: S3-compatible object store

9) If you need third-party product data

You may be able to use product data APIs from:

• affiliate networks
• marketplaces
• structured product data providers
• retail intelligence platforms
• catalog enrichment services

If you want, I can help you with any of these next steps:

1. Design a full API spec
2. Create a canonical product schema
3. Build a source-matching strategy
4. Recommend specific product data APIs for your niche
5. Sketch an MVP architecture

If you tell me your niche and target sources, I can propose a concrete API design.